Dank or not? Analyzing and predicting the popularity of memes on Reddit
- Kate Barnes1,2,
- Tiernon Riesenmy1,3,
- Minh Duc Trinh1,4,
- Eli Lleshi1,5,
- Nóra Balogh1,6 &
- Roland MolontayORCID: orcid.org/0000-0002-0666-52791,7,8
Applied Network Sciencevolume 6, Article number: 21 (2021) Cite this article
Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.
Over the past decade, Internet memes have become a pervasive phenomenon in contemporary Web culture (Laineste and Voolaid 2017). Due to their popularity, memes have received considerable attention in areas such as pop culture, marketing, sociology, and computer science (Bauckhage et al. 2013; Journell and Clark 2019). In the time of the COVID-19 pandemic, memes have become an even more important part of social life since due to social distancing orders more people turned to the Internet for everyday interactions. As a result, Web culture is moving faster than ever and social media sites have exploded with coronavirus memes as people all over the world try to take this serious situation with a pinch of humor (Bischetti et al. 2020).
The increasingly participatory nature of the Internet has made memes into a social phenomenon, created, altered, and spread by Internet users themselves. Today, memes are not only a source of humor but also draw attention to poignant cultural and political themes (Brodie 2009). Memes tend to reflect pressing global issues and while they are not always loyal to the facts (Simmons et al. 2011), they often show what the public is noticing most. Many authors have explored the social network factors that lead a meme to go viral but bracketed the impact that meme content may have on popularity (Gleeson et al. 2015, 2014; Weng et al. 2012). In other areas of human achievement viral success is closely linked with merit (Yucesoy and Barabási 2016), but it is unclear what characteristics lead a meme to have merit. This paper investigates the relationship between a meme’s content, excluding social network features, and its popularity. Along the way, it exposes what topics were popular on the Internet during the global COVID-19 pandemic.
Our paper joins a growing body of literature that employs network science and data science techniques to predict the popularity of Internet memes (Weng et al. 2012; Maji et al. 2018; Tsur and Rappoport 2015; Wang and Wood 2011). Here we analyze the popularity of coronavirus memes based on 129,326 records scraped from Reddit, the largest social news and entertainment site. The main contributions of this work can be summarized as follows:
Using advanced machine learning techniques (such as convolutional neural networks, gradient boosting, and random forest), we perform a content-based analysis of what makes a meme successful, considering several features from both text and image data.
We stand apart from other authors by investigating whether the success of a meme can be predicted based on its content alone, excluding social network factors.
We not only study what makes a meme viral, but we also analyze what incremental predictive power image related attributes have over textual attributes on memes popularity.
Our study provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic.
The term “meme” precedes the digital age, stemming from the Greek mim-ma, something imitated. Thus, memes are pieces of cultural information that remain relatively unchanged as they are passed between individuals in society through imitation. In the modern age, the term has been co-opted by Internet users to mean snippets of information that self-replicate on the Internet (Dawkins 2016; Shifman 2014). When memes took the form of hashtags, tweets, photos, quotes or jokes shared repetitively on the web they became highly visible and a common source of data for social computer science researchers. They are transmitted from person to person through social media sites, online news, or blog posts and can reach extremely large audiences in short amounts of time. These viral memes are important, shared social phenomena. They can represent common opinions, cultural norms (Dynel and Messerrli 2020), carry political power or motivate social change (Dynel 2020; Simmons et al. 2011; McClure 2016; Du et al. 2020). Humorous content may play a crucial role in the spread of memes as it encourages user interaction and creates a sense of in-group connection (Vásquez 2019). However, little is understood about what kind of information is so appealing to Internet users as to become viral. Ours is among few studies that places the content of memes under scholarly analysis.
The journey of Internet popularity is commonly framed in network science as competition between memes for limited user attention (Gleeson et al. 2014, 2015). Memes are analogous to genes (Wang and Wood 2011), cultural fragments passed down through generations. In itself, the Darwinian frame through which memes are understood recognizes the importance of meme content. However, most studies focus on how memes diffuse through online social networks (Wang and Wood 2011) taking into account user interests (Weng et al. 2012), memory (Gleeson et al. 2015), and other social factors.
Many studies have successfully predicted the viral Internet memes based on social network factors (Maji et al. 2018; Weng et al. 2014) and others have designed mathematical models that closely align with the actual transmission of memes through the Internet (Weng et al. 2012; Wang and Wood 2011; Bauckhage 2011). Even when measured in many ways, meme popularity displays a long-tailed distribution. Few memes actually become viral, and most are only appreciated by a few tens of people (Gleeson et al. 2015). Memes distributed in more diverse and well-connected audiences are more likely to go viral (Weng et al. 2014). Additionally, people are more likely to share memes related to content that they have shared in the past (Weng et al. 2012). All of these studies put forth neutral models: they assumed no inherent advantage in terms of memes’ attractiveness to individuals.
In addition to social network factors, the content and formatting of a meme can effect its popularity. Tsur and Rappoport (2015) analyzed hashtags on Twitter, and found that brevity is the most important feature for the memes’ popularity followed by certain legibility characteristics such as capitalization. Berger and Milkman (2012) found that more emotionally arousing text segments from online news are more likely to go viral . Our analysis of meme captions’ length replicates the finding by Tsur et al. but our meme sentiment analysis differs from the Berger and Milkman finding. Others note that there is ever increasing engagement with political memes among adults on the Internet and express concern that political memes will be used to promote extremism or spread misinformation. According to a recent look at Twitter data, 30 percent of image-with-text memes contain political content (Du et al. 2020). There are also disparities among what political and demographic groups share those memes (McClure 2016).
While many papers investigate short text data like hashtags (Tsur and Rappoport 2015; Weng et al. 2014), quotes (Simmons et al. 2011), and Google searches (Wang and Wood 2011), few look at the combination image-with-text memes we consider here. Qualitative studies describe the symbols used in meme sub-genres and how their used but do not analyze the impact of these symbols on the memes popularity (Dynel 2016; Dynel and Messerrli 2020). A study by Bauckhage et. al. models how users’ attention to image-with-text memes fluctuates over time (Bauckhage et al. 2013). It shows that evolving memes (slightly different versions of the same meme) are more likely to gain popularity and stay popular for longer. Du et al. (2020) only study the text within image-with-text memes, claiming that the image is merely a neutral background or further emphasizes information already addressed in the text. Our paper contests this claim. Another study by Khosla et al. investigates the content and social contexts of popular images alone, using data from Khosla et al. (2014). They found that certain colors, low-level image properties like hue, and represented objects correlate with increased image popularity. However, popularity on a photo-appreciation sight like Flickr is much different than the social undertones that go into memes. In our model, similar features to those considered by Khosla et al. show different relationships to image popularity.
Our study considers the widest array of content-based attributes in image-with-text memes so far. Furthermore, our data represents the intense political moment at the start of the global coronavirus pandemic.
Data description and preparation
All data for this project were collected from Reddit, the so called “front page of the Internet.” More precisely, the image-with-text memes came from the largest meme subreddits, namely r/MemeEconomy, r/memes, r/me_irl, r/dankmeme, and r/dank_meme. The subreddits represent communities devoted to the creation of memes and consequently, the development of a shared sense of humor on Reddit. Most popular Internet content first went viral on Reddit, hence the websites catchphrase, so popular memes from these subreddits are likely representative of the content on many other Internet sites too. Additionally the strict etiquette implemented by the Reddit community and moderators ensures that posts align with the subreddit description (Sanderson and Rigby 2013). Thus, only image-with-text memes populate the five subreddits from which we scraped data.
We employed the Pushshift API (Baumgartner et al. 2020) to scrape data from posts in the five meme subreddits. In total we scraped 129,326 unique posts from March 17th, 2020 to March 23rd, 2020 which constituted the beginning of the global coronavirus outbreak. For each post we retrieved the features found in lines 1–10 of Table 1. Additional features such as urls to access the post on Reddit and unique meme ids were scraped as well, but only the features we use for analysis are included in Table 1. Likewise, the features downvotes, meme awards, and posting author were scraped from Reddit and eliminated early on because they were incomplete, populated mostly with zeros. Many of the features scraped from Reddit metadata were already numerical, such as created_utc and ups. The categorical features is_nsfw and subreddit were one-hot-encoded into a numerical representation.
We further processed the meme images, titles, and text from the images to enrich our feature set with more content-based features. These extracted features are listed in lines 13–22 of Table 1 and discussed in more detail in the “Models and results” section. In the process of extracting the content-based features, we made a GET request on each link and observed the status code. Any post with a link that returned a 404 or other similar error was removed from the data set in order to avoid evaluating dead links. Further, any post with a media other than images, such as gifs, was removed as we only wished to consider image-based memes. These cleaning steps resulted in a total of 80,362 records for training and testing the machine learning models. After numerically encoding the image and text based content features that will be discussed in detail in the next couple sections, there were a total of 97 data attributes.
Some conclusions could be made based on the Reddit metadata alone. The created_utc feature contains the timestamp when the post appeared on Reddit in the Coordinated Universal Time zone (UTC). Since most active Reddit users reside in the USA (Tankovska 2020), we converted this to North American Central Time Zone. Based on this feature we created a categorical feature representing the time of day, in four hour increments, when the post was created. The bottom subfigure of Fig. 1 shows the effect of time of day on the normalized upvotes that posts received. Posts published on Reddit from midnight to noon Central US time have a higher chance to attract great attention. This result could mean that most upvotes on Reddit are accumulated during the course of the day, in USA time zones. The memes posted during daytime (Central US time) have more chance to receive moderate attention, while memes posted at night are more exposed to extreme events, meaning that receiving very low attention or great attention. The observation that memes posted at night have more chance to be dank is in line with the phenomenon that was observed by Sabate et al. (2014) based on the analysis of popularity of Facebook content. The authors argue that if content is posted during periods with low user activity (at night), when users will connect in peak hours the post appears at the top of the news wall, that makes it more likely to be liked, commented or shared.
The more subscribers, the more social exposure, so the number of upvotes a post received was likely influenced by the number of subscribers to the subreddit where it was posted. In our data, r/memes has the most subscribers, around 10,000,000, followed by r/me_irl with around 4,000,000, and r/Meme_Economy with around 1,000,000 subscribers; r/dank_meme and r/dankmeme have the least subscribers, less than 500,000 and less than 1000 subscribers respectively. Indeed, we can observe a positive correlation between upvotes and subscribers, as the subreddits with more subscribers tend to get more upvotes (see Fig. 1 upper subfigure). To confirm this observation, we determined the median number of upvotes for each respective subreddit and calculated a Pearson correlation coefficient between these values and the number of subscribers for each subreddit. We received a value of 0.977 for the Pearson correlation coefficient, which indicates a strong near-linear relationship between the upvotes of a post and the number of subscribers to the subreddit. To eliminate this network effect, we normalized the number of upvotes by dividing by the number of subscribers from the respective subreddit where it was posted. In modifying the upvotes feature, we were able to better gauge the popularity of a meme based upon its content alone.
The viral nature of image-and-text memes on Reddit makes this data well suited for a binary classification task. The distribution of normalized upvotes follows a long-tailed distribution: most memes received few upvotes while few memes received many upvotes as shown in Fig. 2. Therefore, viral memes usually differ by two or more orders of magnitude from not viral memes, as defined by our binary classification label, called dank or not in Table 1, and used for the supervised learning models. Using the the normalized upvotes feature as our criteria, any posts with a normalized upvotes value in the top 5% of all posts was classified as dank (positive label, 1), and the rest were classified as not_dank (negative label, 0). Our data set contains 4019 dank entries, and 76343 not_dank entries. Formulating our prediction labels in this way assured that we investigate the phenomenon of viral popularity (rather than moderately successful or mediocre memes) as proposed in the introduction.
We will use three supervised learning models to predict whether memes fall into the dank or not dank categories: gradient boosting, random forest, and convolutional neural network models. The former two use the entire feature set described in Table 1 for training (except the media link feature). The neural network model uses only the meme images, accessed via the media feature, as its input and it is based on a smaller sample of data records. This subset of data will be discussed in more detail in the “Transfer Learning with convolutional neural network” section.
Models and results
In this section we present the results of our analysis. First, an explanatory analysis is provided for the textual and image related attributes with a focus on the impact they have on meme popularity. We also present feature engineering steps. Next, we briefly describe the applied machine learning models together with their performance in predicting the success of memes.
A large portion of the humor and meaning of memes are contained in the text which appears inside a meme image. This text differs from the caption of the meme which was written by the user who created the post and can be scraped directly from Reddit. Both the caption and the text contained within the meme itself may affect popularity. In this section, we study the predictive power of the attributes derived from the caption and the text extracted from the images on meme popularity.
The text from the images was extracted using Optical Character Recognition (OCR) (a9t9 software GmbH 2020). We combined the text obtained by OCR with the caption of the meme, to gather all text associated with a meme. Then we performed tokenization, lemmatization, and stemming to simplify all of the words. This was done using the NLTK and gensim Python libraries (Rehurek and Sojka 2011; Loper and Bird 2002). Tokenization is used to split the text into a list of words, make all characters lowercase, and remove punctuation. Words that have fewer than 3 characters and stopwords were removed. Words were lemmatized so that all verbs occur in their first person, present tense form. Finally, words were stemmed, or reduced to their root form. For example, the “processed words” extracted from three memes using OCR, tokenization, lemmatization, and stemming can be seen in Fig. 3.
Using the processed text data we can extract some potentially predictive attributes such as sentiment and word count. First, we calculated the sentiment scores that quantify the feeling or tone of the text (Liu and Zhang 2012). If the text is positive or happy, it scores closer to 1, and negative or sad texts score closer to 0. Examples for different sentiment scores are shown in Fig. 3. The sentiment model we used to analyze the processed meme text uses a recurrent neural network known as LSTM (Long short term memory) (Shreyas 2019). This network remembers the sequences of past words in order to make predictions about the sentiment of new words. The model was trained on dictionaries with hundreds of thousands of words that were already scored for sentiment.
Figure 4 illustrate the relationship between the extracted text features and the normalized upvotes. The framework in which memes compete for limited user attention suggests that users may respond best to memes with shorter texts. Indeed, we found that the amount of text a viewer is required to read correlates negatively with upvotes. This is in alignment with the findings of Kruizinga-de Vries et al. (2012) on the popularity of brand posts in the social media.
In Fig. 4 we can also observe that neutral memes perform better than extreme ones, but of the extremes, negative sentiments perform better than positive sentiments. These results contradict previous finding that online news content which evokes high arousal, especially negative, is more viral than neutral content (Berger and Milkman 2012). Another paper found that popular memes tend to be unique, in terms of sentiment and other features, whereas memes that are similar to most other memes perform poorly (Coscia 2014). It is unlikely that humor is usually helped by neutral charged content, instead its associated with surprise which is related to arousal (Chandrasekaran et al. 2015). This result suggests that the jokes in memes particularly are about mundane, not arousing topics.
The words extracted from the text were encoded as numerical attributes and analysed for their relationship with meme content and popularity. Similar groups of words such as “coronavirus”, “virus”, and “pandemic” were grouped together under one name. The 7 word categories can be viewed in Table 2. Then, these categories, along with the top 28 most frequently occurring words in the processed_words attribute (in Table 1), were one hot encoded into 35 numerical feature attributes. In total, including text length, word count, and sentiment scores, there are 38 numerical text features.
A word cloud in Fig. 5 created from every word we gathered indicates certain topics are especially prevalent in the memes from late March, 2020. For instance, “coronavirus”, “toilet paper”, “quarantine”, “work”, “home”, “school”, and “friend” all appear most prominently in the word cloud, though some appear in slightly different versions due to our processing. “Memat” is notably one of the prominent words in the word cloud. A popular meme-making website entitled mematic is used by many Reddit users, and each meme produced from the website contains a mematic watermark to indicate its origin. The watermark was apparently read by the OCR as text from the meme. Hence, “memat” is one of the more prominent texts found among the memes. The largest words in Fig. 5 shows that current events do play a great role in the content of memes, though whether this sort of content has a great effect on popularity is another question. An initial analysis showed than in most cases, the words included in table 2 are just as prevalent in the top 5% viral memes as in non-viral memes. The largest difference we found was for the category COVID-19 synonyms in which 23% of dank memes contained at least one word from the category and 17% of not dank memes contained a word from that category. We aim to answer this question further in the following sections by studying the importance of these features to machine learning models.
Most images on the Internet are not neutrally charged. Subtle differences in color, definition or setting can convey vastly different meanings to the viewer. In general unique, bright, high definition images with a low depth of field are ranked more aesthetic by viewers (Datta et al. 2006). Additionally, the presence of certain objects in a photo lead to greater or lesser popularity on Flickr (Khosla et al. 2014). However, memes often have comedic, relatable or reactionary value which is not necessarily aesthetic. The importance of image features may differ for memes as opposed to other types of Internet images.
The image is an important part of a meme. An initial analysis of thumbnail area in our data showed that the majority of memes had the largest thumbnail size available on Reddit. The more popular memes also tend to have larger thumbnail areas.
In addition to thumbnail area, we looked at the colors present in the most popular meme images. Color and thumbnail area are examples of simple image features, aspects of an image that are easily interpreted by human viewers. The colors that the human vision system perceives as distinct have larger value coordinates in HSV (hue, saturation, value) color space, therefore we extracted colors from the HSV versions of our meme images. We used an OpenCV image segmentation technique (Stone 2018) to isolate 30 colors, including a small range around the specific HSV value of the color. This range was used to mask the images, revealing only pixels within that color range. The number of pixels in the mask was normalized for images of differing sizes by dividing by the total number of pixels in the image. These color attributes represent the amount of each of the 30 given colors present in the meme images.
Figure 6 shows the amount of each color attribute in the upper 95 percentile of popular memes. In general, muted colors are more abundant than bright colors in viral memes. Perhaps because memes tend to be mundane photos, often blurry in self-made way, unlike professional photography. This result differs from the similar analysis done by Khosla et al. (2014) in which reds and colors that are more striking to the eye showed the greatest importance. However our results both present blues and greens as less important. Another paper found that images with animate objects tend to be ranked as more funny than images with inanimate objects (Chandrasekaran et al. 2015). Some of the colors found in the most popular memes may be colors that are more common in animate things like animals or human skin and hair tones. Black and off-white were also most present in the bottom 5% of least popular memes, but other parts of the color profile differed. Greens and especially blues were more abundant in these memes, and some shades of orange and brown with large values in Fig. 6 were not present at all in the least popular memes.
In addition to colors, we extracted the average hue, saturation, and value components of the meme images. These are low level image features because while HSV mimics the way human (and now computer) vision works, these components of an image are not always obvious to the viewer. The relationship between these attributes and meme popularity is visualized in Fig. 7. Hue and saturation show a slight negative correlation with upvotes, indicating that yellow-green hued, less saturated images have a positive impact on popularity. Value shows a slight positive correlation, indicating that images with higher value, more distinct and less dark colors may get more upvotes. These features tended to have significant predictive power in the machine learning models.
We also analyzed high level image attributes that aim to describe the semantic meaning present in images. By processing the images with the pre-trained Keras’ VGG-16 neural network, we were able to roughly identify what objects are present in the meme images (Chollet et al. 2015). Figure 8 shows the neural net’s meme content predictions, with the associated probability of that prediction. This categorical data is not necessarily accurate, but does convey some level of information about the subject matter of each meme image. Table 3 lists which VGG-identified content was most common in the top 5 percent most popular memes and lower 5 percent least popular memes. These two columns list the top 10 unique values in each of these groups. The most and least popular memes also shared some VGG-identified content, such as the categories website, comic book, and book jacket. This is not surprising as many memes are created using meme-making websites like Mimetic. The top ten shared content categories are listed in the third column of Table 3. Many of the overlapping categories reflect the formatting of the meme and these were the most common categories identified by the VGG-16 neural network across all of the data records. Because terms about the image formatting were so common, we combined these terms into one category called formatted. The neural net identified specific objects within the images less frequently, but these observations, as shown in Table 3, did tend to differ between the most and least popular 5 percent of memes.
While much of the VGG-identified content referred to miscellaneous items, some of the top categories related to the growing culture around the COVID-19 pandemic. Along with toilet_tissue, lab_coat and mask were within the top 40 most common VGG-identified components in the whole dataset. Many medical masks such as are worn to prevent the spread of COVID-19 were also misidentified as muzzels, gas masks or neck braces by the neural net. Thus these components were combined under one numerical attribute category, masks.
The categorical VGG-identified data was converted to numerical data in a number of ways. Upon observing that many of the VGG-identified objects belonged to similar categories, such as the meme formatting and masks mentioned before, we grouped these into 9 VGG content categories: animals, formatted, sports, clothes, masks, technology, violent content, food and vehicles. Note that some of the content identified in Image 8 would be encoded in one or more of these categories. The categories were then one-hot-encoded into numerical features columns along with the next 8 most common VGG-identified content. These features were somewhat sparse, as the binary one-hot-encoding indicated whether or not a certain vgg prediction, or category, was found in the top three vgg content predictions for the meme. In addition to the binary features, we included the probabilities associated with the top 3 vgg content predictions for an additional 3 vgg related features. These probabilities tended to be ranked as important to the machine learning models discussed in the nextt section.
After these alterations to the raw image data, there were a total of 53 numerical image attributes. The abundance of features leaves room for fine-tuning and eliminating some of them to improve the models. Here, we suggested that certain colors and objects may be associated with viral memes, but the machine learning models will provide more clarity as to what characteristics are actually influential in determining the popularity of a meme.
Gradient boosting and random forest
We selected Gradient Boosting and Random Forest models to perform the binary classification task of placing a memes in the dank or not_dank categories. Both models are ensemble learners that benefit from the accumulated results of weak-learners. The models are trained and tested using the full array of data attributes listed in Table 1, and discussed in the image and text analysis sections. They make predictions based on the same set of labels in which viral memes in the top 5% of normalized upvotes are considered dank, labeled 1, and the rest are not_dank, labeled 0. By observing how these ensemble models make their predictions we can garner insights about the most important features that make memes go viral. Using two models for this task will further validate our results.
Gradient boosting is an ensemble method of weak learners with the optimization of a loss function (Natekin and Knoll 2013). Boosting models fit a new learner on the observations that the previous learner could not handle. The model serves as a good classifier for rank, which suited our binary classification task. The gradient boosting classifier of sklearn’s ensemble package builds in a forward stage-wise manner, which means that a user-defined number of regression trees are fitted on the negative gradient of either the binomial or multi-nominal deviance loss function at each stage and the weighted sum of the learners will be the output (Pedregosa et al. 2011).
The Random Forest is an ensemble method made up of many decision trees. The success of the ensemble depends on the strength of the individual trees and the level of dependence between them. This model is a good choice for our data set because it performs well with a mix of categorical and continuous features, it can handle many features and large amounts of data without risk of over-fitting, and the tree structure is easily interpreted (Breiman 2001). It is quite similar to the Gradient Boosting model, meaning they can be easily compared, and the differences between the models serve to reinforce our results, as our findings are replicated by two models.
Performance and features importance
A limitation to the Random Forest and Gradient Boosting ensemble classifiers is that in their original form they do not perform that well with unbalanced data (Brownlee 2020; Liu et al. 2017). However, many methods for learning unbalanced data with these ensembles have been developed (Chen and Breiman 2004). We modified the models to reduce the effect of skewed data, and generally improve the prediction results. Firstly, we used the BalancedRandomForestClassifier from imblearn as our Random Forest model. This classifier uses random undersampling to train on more balanced subsets of data by resampling data from the training set for each tree classifier in the ensemble. The distribution of positive and negative labels in the training sets can by controlled by the parameter sampling_strategy which represents the proportion of majority to minority class labels. Both the Random Forest and the Gradient Boosting models used 5-fold cross validation, the class weight parameter, and GridSearchCV from sklearn to fine-tune the classifiers’ parameters.
Following these modifications, we split the data into a 53,843 record training set and 26,520 record test set, a 67–33% split. Both models predicted labels on the test set with an AUC of around 0.7 as shown in Fig. 9. Accuracy, recall, precision, and F-1 scores for the highest performing (Random Forest) model can been seen in Table 4, and scores for the gradient boosting model were quite similar. Both models performed poorly in precision. Consistently, the models predicted a larger proportion of positive labels than was realistic for the data set despite the measures we took. Some of the measures we took to to counteract this effect, such as re-assigning thresholds, were adjustable at a cost. Increasing the probability which was sufficient for a positive label would improve the model’s precision but adjusting too much led the recall and accuracy scores to decrease.
The difficulty of predicting the imbalanced data, indicated by low precision scores, may be due to the lack of social network features. Perhaps while content-based features can predict whether a meme has a chance at going viral (has merit), social network features are what determines which of those memes actually do go viral. This supports Barabási’s theory on success in general in which merit is the first step to becoming successful, but social networks determine who among those with merit becomes a superstar (Yucesoy and Barabási 2016).
In addition to the modifications listen above, we tried a few undersampling methods. We performed a 67–33% train-test split for all models. The undersampling results for the best performing models, the Random Forest, are listed in Table 4 and the results for the gradient boosting model were very similar. Using the random undersampler module from sklearn, we undersampled only the training data. This did not have a large effect on the models’ performance, indicating that the other measures we took to counteract the imbalanced dataset, and generally improve results, were effective. We also tried undersampling both the test and train data, this improved the precision, and consequently F-1 scores immensely but had only a small effect on our AUC. (Of course, changing the distribution of the test set alters the nature of our prediction goals, therefore we do not report on the results of these tests extensively). We also note that while the precision value might look quite small without adjusting for the unbalanced distribution, but it means a more than 70% improvement to random guessing dank memes.
The features importance plot in Fig. 9 shows the relative importance of the data features from Table 1 for the Gradient Boosting and Random Forest models trained without undersampling. The two models showed similar features importance, with some variability. Additionally, many of the points explored in earlier sections about the features’ relation to upvotes are reinforced by the importance scores. Simple features such as text length and image size (thumbnail.height) showed great importance for predicting viral memes. The important colors in Fig. 9 also align with the most abundant colors in Fig. 6. Gray, off-white, and pure-black are some of the most important colors for the model and are most abundant in viral memes. Figure 9 also indicates that overall more image-based than text-based features are important. However, this could be due to the fact that we included more image based than text based features overall, 53 as opposed to 38.
Incremental predictive power of image and text features
In addition to the most important features shown in Fig. 9, we investigated whether image or text features have more predictive power for determining viral memes. We used the Gradient Boosting and Random Forest models discussed previously with the full amount of train and test data. Differing from the earlier analysis, we trained four Gradient Boosting and four Random Forest models, each of the four with different subsets of features. The models are trained with image-only attributes, text-only attributes, both, and all attributes from Table 1 to show the incremental predictive benefit of these feature groups. The viral nature of memes makes predicting high performing memes more difficult, but since the skew is an inherent part of the data we decided against undersampling the data for this part of the analysis as changing the distribution alters the nature of our prediction question.
As for the previous models presented in Fig. 9, the models were trained with a set of 53,843 and test set of 26,520 data records, a 67–33% split. All of the modifications and fine-tuning, including class-weight and GridSearchCV, efforts used in those models are the same here. The exact feature description differs slightly between versions of these models due to fine-tuning efforts in which certain colors or processed words may have been eliminated if they showed no importance to the model. These slight differences did not disrupt the organization in which the four models had either only text related features, only image related features, both or all features including social network features scraped from the Reddit metadata such as subscribers.
Figure 10 shows the results of the incremental predictive ability analysis. Not surprisingly, the model trained with all data outperformed the other models. This aligns with previous results in which text and network data held more predictive power for image popularity on Flickr (Khosla et al. 2014). It is impressive that adding only four network features (subscribers, created_utc, is_nsfw, and time_of_day) increased the AUC by 0.02 for the Random Forest Model. Given the results that social network features have shown in predicting meme popularity in past papers (Weng et al. 2012, 2014), it is likely we would have seen a much greater increase in AUC if we had included other social network features, too. Surprisingly, it is not obvious whether image related or textual attributes have the stronger predictive power since the Random forest model performed better with the image related attributes, while the Gradient Boosting model performed better with textual attributes. However, it is clear that they both have incremental predictive power over each other in both models.
Transfer learning with convolutional neural network
Convolutional neural network
Convolutional neural network (CNN) is a class of artificial neural networks that has gathered attention in recent years due to its versatility and ability to achieve excellent performance in a multitude of problems. Among others, it had been used by computational linguistics to model sentences’ semantics (Kalchbrenner et al. 2014), by radiologists to segment organs (Yamashita et al. 2018), by ophthalmologists to identify diabetic retinopathy in patients (Gulshan et al. 2016). CNN’s success lies in its architecture that allows it to learn inherent spatial hierarchies from its training data through recognizing and learning low-level patterns that build up to high-level patterns (Yamashita et al. 2018). This ability to extract important features means that the CNN is able to identify different levels of image representation and capture the relevant ones in the training data, making this model family especially suitable for computer vision tasks (Jogin et al. 2018). Past research has shown that CNN is also able to perform well when it comes to identify an image’s popularity (Khosla et al. 2014). Following this vein of research, in this section, we examine whether we could classify a meme’s dankness solely based on raw image data, ignoring the attributes that we used in previous sections.
Sampling the dataset
The dataset contained approximately 76,000 downloadable images. Because of the imbalanced distribution in posts’ upvotes as can be seen in Fig. 2, we chose to make the not_dank class to be the same size as the dank class by randomly sampling from the 70,000+ images in the not_dank class. We then divided this sub-dataset into training set, validation set, and test set in the following ratio: 50%, 25%, 25%. The exact number of images used in each set is shown in Table 5.
Deep CNNs normally require a larger amount of training data than we had. Previous research has shown that in cases where there is limited training data, transfer learning is an effective method to significantly improve the performance of the neural network (Tammina 2019) as well as reduce overfitting (Han et al. 2018). Several transfer learning methods have been proposed throughout the years. Here, we adopt the method proposed by Yosinski et al. (2014): using the top layers of the pre-trained CNNs as feature extractors, then fine-tuning the bottom layers with our own dataset, and adding a set of fully connected layers for prediction. The main reason we used this approach is the domain difference between our dataset and the ImageNet dataset which makes it necessary to retrain some of the last layers.
The pre-trained CNN models that serve as our feature extractors were trained using data from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) (Russakovsky et al. 2015). This dataset consists of roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images in 1000 categories (Krizhevsky et al. 2017). The pre-trained CNN models we picked—namely, InceptionV3, VGG16, ResNet, Xception, MobileNet—are top performers in previous ILSVRC competitions, and their weights trained with this dataset are all available in Keras (Chollet 2015). Out of these models, VGG16, InceptionV3, and Xception proved to be the best performing feature extractors for our dataset. For further information about the models see Table 6. We will provide more details about how we fine-tuned each of these neural networks in a later section.
Image data augmentation
Data augmentation is used to expand the dataset by generating and including similar yet slightly modified entries in the training process. In regard to image recognition tasks, the most traditional methods are to add noise or to apply affine transformations (e.g. translation, zoom, rotation, mirror, flip) (Suk and Flusser 2003). Previous research has shown that this procedure could reduce error rate, helps with overfitting, and allows the model to converge faster (van Dyk and Meng 2001). Yosinski et al. (2015) has reported that after augmenting the dataset with randomly translated images, their model see a decrease in error rate from 28 to 20%. Another example is in the design of the VGG16 neural network that was among the winners of the ILSVRC 2014 competition, Simonyan and Zisserman also employed image augmentation techniques such as flipping the images, including randomly cropped patches of the images, or changing color intensity (Simonyan and Zisserman 2014). The authors claimed that this data augmentation helped decreased the error rate by 1%. Similarly, our best three models are all trained using a dataset augmented with the following Keras ImageGenerator transformations:
Rescale the pixel values (between 0 and 255) to the [0, 1] interval.
Zoom into the image randomly by a factor of 0.3.
Rotate the image randomly by 50 degrees.
Translate the image horizontally randomly by a ratio of 0.2 factor the image width.
Translate the image vertically randomly by a ratio of 0.2 factor the image height.
Shear the image randomly.
Flip the image horizontally randomly.
Since each network has different architectures, we needed to employ different fine-tuning strategies to each of them. The fine-tuning strategies we used are listed below:
For VGG16, freezing the first three convolution blocks, fine-tuning the weights of all the other layers (in the two other convolution blocks in the network, plus the last three fully-connected layers).
For Xception, freezing the weights of all convolutional layers, and fine-tuning the weights of only the last three fully-connected layers.
For InceptionV3, freezing the weights of all the layers up until the “mixed7” layer, then fine-tuning the rest of the layers in the InceptionV3 network plus the last three manually-defined fully-connected layers.
For all networks, dropout is implemented after the first and the second layer of the last three fully-connected layers.
For all networks (VGG16, Xception, InceptionV3), the last “softmax” layer is removed, and replaced by a “sigmoid” layer for prediction.
The images are resized to fit the default input size for each of the network (299x299 for Xception and InceptionV3, 224x224 for VGG16).
All networks include ReduceLROnPlateau functionality from Keras that reduces the current learning rate by 25% whenever the validation accuracy does not increase in the span of 3 epochs.
All of the neural networks tested for this research were evaluated on the test set using several metrics (Accuracy, Precision, Recall, and F-1 Score). The results are recorded in Table 7. We also calculated the ROC curve of the best 3 models along with their AUC scores which are shown in Fig. 11a. Figure 11b, c show the change in the training and validation accuracy and loss during fine-tuning the VGG16-based model, which produced the best AUC score.
From Table 7 and Fig. 11, we can conclude that the VGG16-based model seems to slightly outperform the other models, while the Xception-based model comes in second, and the InceptionV3-based model in third place. We can also conclude that the best neural network (AUC = 0.63) performs equally with the best performing ensemlbe model trained on hand-crafted image features (AUC = 0.63).
From our experiments with different models, we have observed that using image augmentation helps with making the models converge faster and achieve a higher accuracy. Fine-tuning the last few layers of the CNN models with the transfer learning methodology also improved the performance of our models. The over-fitting issues we encountered were depressed by adding a dropout rate between layers and reducing the learning rate between epochs. Although we have experimented with several models and parameters, the model performances show that it is hard to predict the dankness of image-and-text memes using the image content alone. This finding is in line with similar previous research where image-content has smaller significant compared to social contexts and other features for predicting image popularity on Flickr (Khosla et al. 2014). The difficulty of content based popularity prediction of memes is also illustrated by the memes where the model prediction does not match the true class label (see Fig. 12). It is a difficult task to tell the true class of these memes both for humans and machines.
In this paper, we analyzed image-with-text memes collected from Reddit. Using machine learning models we investigated whether viral memes can be predicted based on their content alone. We considered the problem as a binary classification task defining viral memes as the top 5% of all posts in terms of upvotes. Our best performing model is a random forest model that performs moderately well with an AUC of 0.6804, accuracy of 0.6638, precision of 0.0854, recall of 0.5897. While the precision value might seem quite low at first sight, it is a 70% improvement to random guessing dank memes.
Moreover, we studied the most important features and we found that gray content, image size, saturation, and text length have the greatest impact on the prediction. While there was a lot of COVID-19 related content in the dataset overall, visible in the word cloud, and some vgg-identified image content, features related to COVID-19 proved less important to the performance of the models. Thus, we estimate that while memes often reflect pressing world issues, the presence of this sort of content has little impact on whether memes will go viral. We also investigated the predictive and incremental predictive power of image and text features. While we cannot conclude whether image related or textual attributes are the stronger predictors of a meme’s success, we have shown that they both have incremental predictive power over each other. If we use only the images as an input with a convolutional neural network we can reach AUC = 0.63, and that agrees with the performance of the best performing random forest model trained on hand-crafted image features. Comparing our results with other works where social network and community features were also used for predicting popularity (Weng et al. 2012, 2014), we can conclude that while the content-based analysis can also predict success with reasonable efficiency, social network features could improve the performance significantly. While content based features could predict memes with merit, social network features determine which among those with merit actually go viral.
It is also fair to acknowledge some limitations of this study. Due to the the short time period in which we collected data—in the intense moment at the beginning of the coronavirus outbreak—our results cannot necessarily be universally generalized. However, we believe that many of our findings are relevant for meme popularity in general. Moreover, the short time period of the collected data did not allow us to study the temporal and dynamic aspects of meme success or identify so-called “sleeping beauties”. The latter is a phenomenon of information spread in which a meme will remain unnoticed for a long period and then suddenly spike in popularity long after it was originally posted (Zhang et al. 2016). We propose these aspects of meme popularity prediction for future research. Furthermore, an other stream of relevant future research would be to analyze memes inspired by COVID-19 alone.
Availability of data and materials
The datasets analysed during the current study are available in the GitHub repository: https://www.github.com/dimaTrinh/dank_data.
Hue saturation, value
Optical character recognition
Long short term memory
Receiver operating characteristic
Area under the curve
Convolutional neural network
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Unconventional Linking: Dank Memes
As lovers and researchers of all things cultural, it’s about time that we wrote about our perspective on the pervasive and cavernous culture of Internet memes.
From message boards and email chains in the nascent stages of the Internet to full-blown “normie” (mainstream) adoption on Facebook and Instagram, memes have come to define the culture of the Internet and its many nooks and crannies. Seamlessly replicating and evolving for communication and entertainment, these viral inside jokes and commentaries provide a lens to see the outlook of generations, an explosive advertising opportunity, and an intriguing resource for qualitative research.
Before we get ahead of ourselves, what exactly is a meme?
While colloquially a “meme” typically refers to an image with text on it, like we’re used to seeing from cultural icons @fuckjerry or @thefatjewish, anthropologists see Internet memes more broadly as the spread of ideas or development of culture enabled by information technology. Using this definition, the only consistent characteristic of a meme is that it must be an expression of an idea and not a physical object, meaning that memes can be speech, actions, images, gifs, emoticons, tweets, vines, etc. The distinction between an Internet meme and the pre-Internet meme comes from the spread of culture being largely limited to “word-of-mouth” and mimicry before mainstream adoption of the Internet.
With the rise of the Internet, memes have literally gone viral. The effortless copy-paste of digital ideas onto instantaneous global forums has caused ideas to be spread, curated, and appropriated at a previously unimaginable rate. This bring us to what some call the Age of Dank Memes, as stale and irrelevant memes or ideas are pushed or down-voted into obscurity in minutes, while only the spiciest memes rise to Internet fame.
But how can memes actually be used?
Understanding Millennial Angst in the Age of Dank Memes
Millennials, expected to become the highest spending generation in America this year, make up the core of the Internet and are unsurprisingly prolific meme creators. From generation-oriented memes such as “Old Economy Steve” and the infamous and forever appropriated millennial Time Magazine Cover, we can start to understand millennials’ interlinked angst and humor. Making 20% less money than their parents at each career stage, enveloped in a toxically divided political arena, and dealing with an impending environment crisis, it’s not surprising that millennials think its hilarious to eat Tide pods.
To understand what these occasionally morbid and usually absurd expressions mean for the generation, we can glean some insight from the Dada art movement, the unofficial grandfather of memes. Following World War 1, in rejection of traditional values of logic and capitalism, Dada artists expressed existential angst through collages filled with social commentary. The similarity is beyond evident, as both Dadaism and memes create absurd humor in the collaging of popular images to compensate for an overarching frustration with society of the moment. In making that connection, memes are not an expression of millennials’ nihilism, but a way of lightheartedly confronting society’s woes.
Overall, we believe that there are three broad things that we can learn from millennials’ meme culture:
1) Millennials are frustrated and disillusioned with the status of economic and political society
2) The obsession with absurdity and dark humor actually reveals millennials’ resilient mentality in confrontation of these issues
3) As demonstrated by the rate of meme exchange, millennials are decentralizing their information sources and reacting to trends faster than ever
How are brands using memes?
In the ongoing battle between marketing and non-commercial culture, it is unsurprising that brands have turned to these unsuspecting viral tidbits for self-promotion. Essentially, marketers have two approaches to memevertising, either creating original meme content or advertising in a way that is conducive to memification.
One of the most established brands that has turned to meme creation is the luxury icon, Gucci. In its campaign #TFWGucci (that feeling when), Gucci collaborated with vanguard digital artists to make memes to promote a new watch collection. While the campaign elicited an explosive reaction online outperforming their historic social media promotions in reach and engagement, there was significant push back against the luxury corporation’s exploitation of one of the last seemingly authentic or non-commercial mediums of expression. Gucci shows both the arousal power of memevertising and the age-old risk of inauthentic cultural appropriation in marketing.
On the other hand, brands have actually achieved more impactful success in the intentional generation of memefiable content over direct meme creation. Considering Dos Equis’ forever referenced “most interesting man” or Drake’s dad-like dancing in Hotline Bling, we can see how brands are able to fuel the meme conversation without risking exploitation. However, while this approach is less likely to be rejected by your audience, the risk for the brand comes in the complete loss of control in how people will naturally give meaning to your content. Further, the creation of memefiable content requires very specific calibration and formatting – this is one area where we can help!
How can we use memes to help your brand? Memefying research
We are the human experience partners, using unconventional thinking and uncommon sense to help you get to know your audience and find your voice. In delving into a set of subcultural Internet forums that fit your audience and analyzing the trending memes and conversations, we can provide unparalleled insight into several facets of your customer, including type of humor, style of lingo, social norms, and content trends.
With this information in-hand, we can further assist in building your brand’s voice to ensure audience resonance and even judge potential for memification. In addition, we have the knowledge to communicate with your audience in a more natural way to elicit deeper insights or responses in more traditional in-depth interviews or groups.
We would love to design an innovative approach to solving your brand’s challenges! Please just get in touch to talk more or to collaborate.
Also, if you have enjoyed reading this, you can sign up to future Unconventional Linking newsletters here.
The 100 Greatest Memes Ever, Ranked
We live in an era defined by memes. True, meme-ifying images and videos is a practice as old as humanity itself, but the advent of the internet has made that process much... danker. Whether repurposed from YouTube videos, movie screengrabs, or viral catchphrases, these golden nuggets shape how we consume, criticize, and communicate through cultural touchstones.
But which ones have achieved lasting greatness? To answer that, we looked for memes with universality and malleability; a dank meme exists in many permutations, and can cross cultural and linguistic barriers. We confined ourselves to the internet, so only symbols and phrases that crossed from traditional media to the web qualify. (Sorry, Kilroy.) We also considered the ubiquity and persistence of a meme in determining its position on this list, which means more recent memes tend to wind up lower in the ranking, but may rise or fall in future update. As you'll soon see, though, this ranking is perfect and indisputable.
ALSO READ: The Best Memes of 2021
100. American Chopper argument
(Not so) long ago, there existed a Discovery Channel program titled American Chopper, about the Teutul family, a tough bunch of guys who built custom motorcycles. Today, this heated scene from said show, in which Paul Teutul Sr. and Paul Teutul Jr. argue with words and then chairs, is back from the dead as a template for staging new arguments. Regardless of the stakes, the intensity always remains the same, which is very, very good.
What single image better defines the early experience of the modern internet than the Trollface?
98. Keep Calm and Carry On
This OG meme goes all the way back to 1939, when World War II broke out and the British had to get their propaganda machine in gear. Did the War Ministry know it was creating a bona fide proto-meme? No, obviously, but when originals of these posters were unearthed in 2000, they quickly spread around the internet thanks to their simple design (already looks like a macro!) and the irony of the ask. Yes, when the Germans are bombing London back to King Arthur's court, just go about your business! Everything is normal, folks! Like all great memes, this one has reached an annoying super-saturation point, but its influence must be acknowledged.
97. Dat Boi
Though the meme-stream media was charged with killing Dat Boi, the polarizing green frog that briefly invaded Tumblr and Twitter via unicycle, that doesn't mean you have to completely forget about the unexplainable joys he and his brothers gave the world.
96. Large Adult Son/Absolute Unit
Right before the end of 2017, British hotelier David Morgan-Hewitt inspired what some call the successor to the reckless "large adult son" phenomenon, which itself had found a second wind thanks to the exploits of Donald Trump's progeny. "Where the large son is unchecked energy, an absolute unit is the picture of poise," wrote MEL Magazine's Miles Klee, "proof that the seemingly uncontainable aggression of mammoth males can button itself up; that men may break with patrilineal pressure, becoming something other than bumbling junior apprentice. Unlike large adult sons, the unit isn’t bound by the inconvenience of gender. Properly speaking, the absolute unit needn’t be masculine, or even human—just absolute." Behold, marvel away.
95. The Honey Badger
It sounds a bit quaint now, but many (OK, maybe 10?) years ago, absurdist voiceovers could rule the internet. The Honey Badger video is a particularly well-crafted example of this phenomenon, with memorable catchphrases—"the honey badger doesn't give a shit," "honey badger takes what it wants"—becoming so popular that fans coined it as a nickname for then-Heisman Trophy candidate Tyrann Mathieu, who now plays in the NFL. See, Mom? Memes DO last!
94. Woman yelling at a cat
On the left is Taylor Armstrong, a real housewife of Beverly Hills, and on your right is Smudge the cat. Simply put side by side, as Twitter user @missingegirl offhandedly did in May 2019, the Taylor and Smudge screenshots form our new favorite representation of the troll's war.
93. "I'm the Juggernaut, bitch"
The early-to-mid-2000s were the internet's awkward, adolescent phase, with a sense of humor and casual use of words like "bitch" to match. The Juggernaut video—an overdub of an old X-Men cartoon—presaged many of the facts we take for granted now, like absurdist, non sequitur one-liners that exploited the rabid fandom of the comic-book crowd, to the point that "I'm the Juggernaut, bitch!" made its way into X-Men: The Last Stand.
92. Sad Keanu
Keanu Reeves, never change. If you want to sit on a bench looking sad, sit on a bench looking sad. Be who you are on the inside. Be a meme if you want to be a meme. The internet loves you just the way you are.
91. "retire bitch"
In March 2013, actor Danny DeVito tweeted "Antonin Scalia retire bitch." His reasons for doing so are still unclear. But four years later, the concise demand has bloomed into a popular refrain, a call for controversial men, especially politicians, to cut their bullshit and pack it in.
ALSO READ: The Best Memes of the '00s
90. Old Town Road
The yee haw agenda. Yee yee juice. Months as the country's number-one song. A star-studded music video. Near-universal recognition in the elementary school market. Lil Nas X has completely taken over pop culture since his country-trap song became a TikTok sensation in late 2018 to early 2019, quickly jumping across platforms and age groups to achieve ubiquity. Just when you think the steam is running out on the "Old Town Road" train, it just keeps going, proving that Lil Nas X—and this meme—will stay true to the message of the song by riding until they can't no more, proven by massive hit ("Montero") after hit ("Industry Baby").
89. Scumbag Steve
You know Steve. He's the dude who appropriated hip-hop culture in high school, even though he lived in a suburban McMansion. He brought 11 uninvited creepy bros to your friend's party. He smokes all the weed. He's Scumbag Steve, one of the most persistent, durable macro-memes the internet has produced; it won't surprise you to learn that the image first appeared on the cover of an album by a group called Beantown Mafia, which is just as bad as it sounds.
88. Distracted Boyfriend
Shot by Antonio Guillem, the stock photo "Disloyal Man Walking With His Girlfriend and Looking Amazed at Another Seductive Girl" depicts... just that. The Meme Documentation Tumblr traces its birth as an image-macro meme back to as early as January 2017, but it didn't explode until it hit Twitter during summer of the same year, functioning as a metaphor for pretty much anything involving competing desires, getting remixed with other memes, and folding in on itself in that inevitably meta way. "I do not really have the time to follow these things," the guy in the pic told SelectAll, "but for what I have seen, I can say that it's crazy what people can imagine."
87. Michael Jackson eating popcorn
It's actually pretty surprising that Michael Jackson didn't produce MORE meme-able moments, considering he's the King of Pop and once dangled his baby from a balcony. That his sole entry on this list is a reaction GIF people share when they see a public beef percolating online is a testament to his lawyers and PR reps.
86. Wife guys
Here at Thrillist, like everywhere else, we love a wife guy. A wife guy is a dude who posts very dramatic and/or very extra things online about his wife, pretty much JUST to get some of that sweet, sweet attention from millions of strangers. Patient zero, as we remember fondly, was Curvy Wife Guy, also known as Robbie Tripp, who hit send (and keeps hitting send to this day) on a number of photos with lengthy captions about how he's such a great guy for marrying a woman who's not skinny. More recently, the wife guy crown has gone to Cliff Wife Guy, whose video of his wife falling into a ditch preceded by a clip of them both crying about how traumatic the experience was and how your life really can change in an instant warmed the hearts of all of us who were just glad he was there, not to catch his wife by the arm or break her fall, but to film the whole thing and upload it to YouTube. There are so many wife guys out there; please, never stop posting about your wives.
85. Darude's "Sandstorm"
What song should I listen to? The answer is always Darude's "Sandstorm," the internet's anthem. If you have to ask, you'll never know. Grab the lyrics here so you can sing along.
84. Is this a pigeon?
No. Though this anime still—from the '90s series The Brave Fighter of Sun Fighbird—has been making the rounds on the internet for years, it's been revived, like a sassy Lazarus, for remixes and Distracted Boyfriend-esque object-labeling. Read more about its legitimately fascinating origins here.
83. "Are you not entertained?"
Well, aren't you? Russell Crowe's iconic line from Gladiatorrings true today. And right now, frankly, as you have 82 more memes to dig through.
82. Grumpy Cat (RIP)
Grumpy Cat was a sweet cat that became a viral cat that became a movie cat voiced by Aubrey Plaza. If digital archaeologists of the future uncover nothing but that sole line of code, it'll be all they need to know about why our society collapsed. We can't stop looking at Grumpy Cat's frown.
Originally, the song behind this video was the story of "a man, Johnny, riding his horse across the American prairie to his sweetheart Mary, who knits socks as she waits his return." Then it lost its lyrics, and now you can send it to anyone you'd like to shut up and/or troll. RIP, Eduard Khil.
80. The Ice Bucket Challenge
In a vacuum, "The Ice Bucket Challenge" would pass without a second look. But since nature abhors a vacuum, it became one of the few examples of a charitable cause using a meme for the power of good. Who would've thought, first of all, that a viral challenge could lead to actual research discoveries. What's more, the challenge succeeded in sloughing off the name "Lou Gehrig's disease" from ALS. Credit where credit is due.
79. Rebecca Black's "Friday"
Remember "Friday"?! Gotta get down on Friday?! The vanity project of a poorly advised but decently well-funded teen created a viral furor on the internet which led to the creation of remixes, gifs, and lasting infamy for Black. Without conferring any of the benefits of fame. Well... actually. We'll see about that.
Ah, THE DRESS. The great equalizer. You thought you were above THE DRESS. You thought you could ignore THE DRESS. You thought you could casually observe without repercussions that THE DRESS is obviously blue and black, only to find yourself embroiled in a three-hour-long argument with your significant other, who thought the dress white and gold, that precipitated the end of your relationship. THE DRESS exhibited no mercy in its overwhelming, brutal dankness.
77. "Chocolate Rain"
Before Rebecca Black, there was Tay Zonday, whose "Chocolate Rain" lyrics became the embodiment of late-W.-era resignation. The song earned itself a feature on South Park as the epitome of the internet's functionality in a capitalist society. What does one do with fame no one's willing to pay for? Like the song says, "Some stay dry and others feel the pain."
Dabbing is newish, but it has some famous exponents, for better and for worse. Cam Newton, for one, sunk his teeth into it. Migos popularized it. And Squidward turned it into the ultimate surprise. But then there were Bill Gates and this sociopath. The jury's still out, but the simplicity of the original could place it in the pantheon of celebrations.
75. Vancouver riot kiss
Have you ever loved someone like these two loved each other in the midst of a 2011 riot, Canada's largest display of violence since Vice Admiral Cantwell made a rude remark about Queen Victoria's corset? No, you haven't. To love like this would mean ignoring the realities of the real world and also having a heart. Photoshoppers agreed, highlighting the effect by placing them in historic situations about which they couldn't care less.
74. Ancient Aliens
The History Channel anticipated the internet's brazen race for eyeballs at all costs, thanks to a comedically liberal interpretation of the word “history.” Eventually, things got so bad that this dude became an "expert" on historic events that, contrary to what you learned in school, were carried out by ALIENS. So, whenever you need an explanation for a difficult question, use this macro and all will be revealed.
73. Tom Cruise jumping on a couch
For Tom Cruise's career, there is Before the Couch-Jumping, when no one questioned the biggest movie star in Hollywood, and After the Couch-Jumping, when his mere presence raises an eyebrow. The profession of love for his then-wife Katie Holmes raised the scrutiny over the Church of Scientology and, well, the rest is history. Meanwhile, over 15 years later, the internet is still meme-ing this brazen display of love.
72. Nick Young ??? ???
Swaggy P's career as a basketball player befuddles those who know him as a shoot-first, overconfident ball hog. And yet he possesses a strange charisma encapsulated in this meme, which serves as a macro AND a reaction to anything as befuddling as Young himself—to make things even more meta, the meme has come full circle.
71. Ceiling Cat
An OG cat meme that has taken on new macro-meaning in the age of constant government surveillance. Ceiling Cat is watching you illegally download NSA documents you acquired through your private contracting gig! Dang.
Hate when your MacBook Pro restarts for no reason? Bored of all your video games? Sick of the ads on Hulu? Go cry about it, Jared Kushners of the world.
68. "Delete your account"
Hillary Clinton ended this meme when she tweeted it at Donald Trump during her 2016 presidential campaign. But its origins as a Myspace insult that migrated to Tumblr that migrated to all social media as the ultimate shut-down retort indicate it has a long, long, long shelf life. "Delete your account" somehow manages to be both nicer and crueler than its wicked cousin, "Kill yourself."
Those of us who lived through the planking craze will tell our grandkids tales about the glory days of 2010 and 2011, when anything seemed possible, any situation ripe for a good ol' plank. Or at least we'll tell our mom's friends when she has them over for dinner, and we decide to come up from the basement to have some wine. This meme stands as the quintessential example of spontaneous brilliance subsequently ruined by others who tried too hard to replicate it. Remember "owling" and "Tebowing"? Yeah, those sucked so hard.
ALSO READ: The Best Memes of 2020
66. "Don't tase me, bro!"
It says a lot about John Kerry's 2004 presidential campaign that this is the most memorable Kerry moment, and it happened in 2007. Overzealous police officers attempted to remove a man who interrupted Senator Kerry's speech at the University of Florida; knowing what would happen next, the protester offered one final, futile plea: Don't tase me, bro! The line has become a response to any unpleasant experience, and its close relative, "Don't tease me, bro," has become an antidote for anxious anticipation the world over.
65. "Netflix and chill"
Future generations, take note: This is right up there with "Playing at St. George." It just means "fucking."
64. "More cowbell!"
Before he was a mainstream movie star, Will Ferrell was the off-kilter backbone of Saturday Night Live, with an eye for the hilariously mundane. The 2000 sketch that generated this now-famous line focuses on the fictional percussionist of the Blue Oyster Cult who rocked the cowbell during recording sessions for their hit song, "Don't Fear the Reaper." In the years following this sketch, as two foreign wars raged and cultural hegemony hit an all-time high, "More cowbell!" became a rallying cry for something—anything—different.
63. James Van Der Beek crying
For those who think memes don't matter: Consider James Van Der Beek, who parlayed his horrendous cry-face in the teen show Dawson's Creek into a universally recognized expression of low-stakes bereavement, and a second, self-parodying act. Well done, Van Der Beek. Well done.
62. "Fuck it, we'll do it live!"
Bill, we're going to remember you for a number of reasons. None of them good, but some more meme-able than others.
61. Auto-Tune the ____
Remember Antoine Dodson? This is him like you've never heard him. Way before Unbreakable Kimmy Schmidt was doing it, other people (with The Gregory Brothers leading the charge) were Auto-Tuning the fuck out of the news and making gloriously awful masterpieces.
60. "Hey girl"/Sensitive Gosling
It really doesn't matter what Baby Goose does, shit will not stick to him. The man played Robert Durst, for God's sake! "Hey Girl" cemented Gosling's legacy as a dream catch for women, who was simultaneously (and somewhat confusingly) sensitive to the modern woman's needs, yet also ready to... be a husband? The Gossiping Gosling moment only added to the actor's meme legend status.
59. "Haters gonna say it's fake"
Thanks to this kid's talent show performance, this other kid's incredible Halloween costume, bottle flipping reached new heights in 2016. The only thing better than watching everybody's daring attempts to pull off plastic acrobatics were the heavily edited fakes that emerged as part of the "haters gonna say it's fake" meme, one that exists well outside bottle flipping.
It's sad this is dead. For a while—including before this trend hopped from real-life frat stars to the internet—one of the great joys in life was watching a bitchin' bro top off a Smirnoff Ice in earnest, then get up and plaster a super-cool look on his face—you know, the kind of smirk that said, "Fuck, man, I'm tragically uncool, aren't I?" Damn. RIP.
57. Arthur fist
Arthur, the PBS educational TV series based on the Arthur the Aardvark books that just ended its 25-year run, has generated some of the best memes. In particular: One Twitter user's insightful take on an Arthur freeze frame led to 2016's greatest representation of inner frustration. It's the gift that keeps on giving.
56. "Imma let you finish"
When Kanye West crashed Taylor Swift's speech at the 2009 MTV Video Music Awards, he probably knew he was doing something that would never be forgotten. But surely he didn't realize the moment would later turn into one of the most enduring memes of all time. It functions almost like the simple setup of a knock-knock joke. You know what's coming next. And it ain't good.
55. Casual-Pepper-Spray-Everything Cop
The Occupy Wall Street movement spawned dozens of social media campaigns, images, political action, and protests. Summing up the confusing melange was an overzealous, dickish campus cop at UC Davis who decided it would be a good idea to stroll up and down a group of peacefully protesting students and pepper spray them directly in the face. Almost immediately, the cop found himself ruining the rest of history.
54. Recut movie trailers
Just a few months after the launch of YouTube, commercial editor Robert Ryang inadvertently kicked off a movement by recutting Stanley Kubrick's The Shining into a lighthearted family comedy. Peter Gabriel's "Solsbury Hill" clearly worked its beaming, inspirational magic; the viral success of The Shining trailer spawned countless recuts, with a few—see: Brokeback to the Future—catapulting the practice to legendary status.
53. Slender Man
The Slender Man—sometimes spelled as one word and occasionally referred to as "Slender"—is a towering, faceless humanoid who dresses in a suit and stalks lonely children. Entirely fictional, he was created in 2009, when a user of the internet forum Something Awful submitted a doctored photo for a paranormal image contest. The character quickly exploded in popularity and became a fixture of horror sites (namely Creepypasta), inspiring countless videos, photos, and pieces of fan fiction. He is the horror meme.
52. "Shit X people say"/Starter packs
Casual stereotyping has fueled the internet for a long, long time. Before we had the starter packs you see today on Instagram and Facebook, we had the slightly related YouTube videos that poked fun at what certain types of people used to say.
51. "You have died of dysentery"
Finding out you or one of your beloved Oregon Trail wagon-mates perished via unrelenting diarrhea was the grade-school equivalent of being audited by the IRS. Still, it’s better to die of dysentery alone than kill your whole family fording a river. Right? OK, maybe not.
50. The Most Interesting Man
As much as we love this guy, and his incredible feats, we hate to break this to him: He's basically the mall version of Chuck Norris Facts (more on that below). We still love you, and you were undeniably huge, my man. Just not quite the real deal.
49. Smash Mouth's "All Star"
This turn-of-the-millennium hit found second life in the irony-soaked hands of a new generation, who find that relentlessly mocking the song is much more pleasing than listening to it. Peak "All Star" is owned by the lovable, neck-bearded Jon Sudano, who poignantly squeezes Smash Mouth lyrics into other popular songs. Smash Mouth, who performed at a COVID superspreader event in Sturgis, North Dakota, had this to say about the meme: "It's funny because a large percentage of our fans don't even know what a meme is—heck, we didn't really know either at first." Yeah. Not the sharpest tools in the shed.
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48. "That's what she said"
The phrase may date as far back as Saturday Night Live's "Wayne's World," but it's still riotously funny to 13-year-olds the world over (and Michael Scott from The Office). Anything can become sexual, and these four words are the proof.
It wasn't that long ago that former Two and a Half Men star Charlie Sheen took the word "winning" out of the sports realm and applied it to life in general. Unsurprisingly, his antics quickly spread to bros the world over and Donald Trump.
46. Star Wars kid
Parodied by such comedy stalwarts as Stephen Colbert and Arrested Development, this 2002 video of a kid wielding a ball fetcher like a lightsaber exploded onto the pre-YouTube internet, repurposed by any and every internet user with video-editing skillz. The bittersweet twist is that viral fame took its toll; after suffering emotional damage, Star Wars kid slapped the cyberbullies who leaked the video with harassment lawsuits.
45. Obama's "Hope" poster
Shepard Fairey's reproduction of an Obama portrait quickly became one of the most iconic images of the 2008 presidential election. It wasn't long before several parodies, imitating the style and minimalist message, spawned.
44. Dick Butt
This one doesn't require that much unpacking. Dick Butt is a drawing of a penis with a mouth, nose, and eyes who also happens to have another penis emerging from its rear end. (This second penis does not have facial features.) It's a dick with a butt. Hence, Dick Butt. It came from a webcomic by artist K.C. Green and was popular on 4chan, YouTube, Reddit, and other places that you might assume would find Dick Butt hilarious. Either you think Dick Butt is funny or you're probably not reading this explanation anymore.
43. Shirtless Putin
Vladimir Putin has been the subject of countless memes over his seemingly eternal reign heading the Russian oligarchy. While "Shirtless Putin" memes—featuring him riding eagles, shootin' guns, and engaging in other real-life and photoshopped badassery—have been Westernized riffs on his over-the-top and well-staged acts of masculinity, the people of Russia have used an image of Putin as a gay clown to protest the regime's harsh stance on LGBTQ rights. This meme (and all memes of Putin, really) were promptly banned by the Kremlin—meaning they clearly got under his skin, proving not all memes have to be vessels for Dick Butt jokes.
Animals make excellent internet fodder, and "Philosoraptor," a popular image meme where a quizzical dinosaur thinks deeply absurd thoughts, is the perfect example of what even an extinct creature can accomplish. Where did this Jurassic meme hatch from? While you'd think this particular joke was cooked up on a web forum, Philosoraptor actually debuted as a T-shirt sold on the website Lonely Planet by a designer named Sam Smith. Yep, that's right: Novelty clothing can still be funny.
WIlliam Shatner's entire existence has morphed into a meme, from the vocal modulation to appearing on Shit My Dad Says, and you can't argue with the results. He's continued to make bank as a octogenarian actor, with this angry Star Trek II: The Wrath of Khan scream echoing through the annals of history.
40. Hitler reacts
Not long after the release of Oliver Hirschbiegel's 2004 film, Downfall—about Adolf Hitler's final days in his Berlin bunker—YouTubers turned the climax into a subtitled burst of absurdist comedy. In the real version, actor Bruno Ganz fumes with German fury over a failed assault. But in many of the viral parodies that followed, he kvetches about trivial pop-culture matters, everything from late-night show politics to Taylor Swift. "The point of the film was to kick these terrible people off the throne that made them demons, making them real and their actions into reality," Hirschbiegel told Vulture in 2010, noting that his favorites were the Michael Jackson and Billy Elliot ones. "It's only fair if now it's taken as part of our history, and used for whatever purposes people like."
39. Double rainbow
"Oh my God, it's so bright and vivid. Oh! Oh! OHHHH!" That's how Paul "Bear" Vasquez described the double rainbow he spotted and recorded near Yosemite in 2010. A beautiful experience to behold, one complemented nicely by Bear, whose commentary made him sound like he was scared, happy, and on the verge of an orgasm. The video was a meme creator's dream, immediately spawning songs and catchphrases, and changing the world forever.
38. Numa Numa
It's fun to watch other people have fun. That's the driving philosophy behind many of the memes, especially the ones that popped up online in the early '00s like "Numa Numa," which emerged from a video of New Jersey resident Gary Brolsma dancing like a mad man to "Dragostea Din Tei" by Moldovan pop trio O-Zone (i.e., the song sampled on T.I. and Rihanna's "Live Your Life"). Brolsma's joyful lip sync inspired countless tributes, parodies, and sequels, but nothing beats the original for pure, unhinged joy.
37. "Bye, Felicia"
It's hard to think that when Ice Cube was shooting Friday, his classic 1995 stoner comedy, he had any idea that a throwaway line like "Bye, Felicia" would go on to become a popular dismissive catchphrase—much less the source for countless GIFs, Twitter put-downs, and even the title of a VH1 show. But that's how the internet works. Once people latch onto a piece of culture, it takes on a life of its own. This particular phrase went from the movies to the internet and then back to the big screen: Friday director F. Gary Gray incorporated the line into his N.W.A. biopic Straight Outta Compton, where Cube's actual son O'Shea Jackson Jr. delivered the line while playing his father. Whoa.
36. Success Kid
Remember how fun it was to watch Kip Dynamite score a huge win? This meme was like that, but on steroids. The cuteness mixed with the often idiotic quips made this macro one of the most-used of its kind. And for good cause—whenever you needed a dose of optimism, Success Kid was there to remind you about how good (or dumb) life could be.
35. Duck face
Whether you saw it first on Myspace, in Zoolander, or on the cover of Little Feat's Down on the Farm, you know damn well what this pose is. Heck, you might've even duck-faced a few times yourself. Never forget.
34. Ate my balls
While this meme has faded into obscurity, it deserves a slot on this list for being one of the first memes to thrive on the internet. The archived (and delightfully '90s) website started by University of Illinois student Nehal Patel as a simple joke, featured Mr T. making various comments, via crudely drawn speech bubbles, about eating his balls, your balls… pretty much everyone's balls by the end. It was an early demonstration in remixing memes, as evidenced by such varieties as Chewbacca Ate My Balls and the still-relevant Bill Gates Bought My Balls. It just goes to show you, the internet has always had an affinity for well-placed genital humor. Hopefully, it always will.
33. Squinting Fry
Who would've thought that a random screengrab from Futurama's "The Lesser of Two Evils" would become so meme-orable (ahem)? Probably not Fry, but here he is anyway, still being used as a macro stand-in for suspicious moments and confusion.
32. "Gangnam Style"
Earwormy passages? Easy dance moves? Absurd imagery? Carefully crafted satire of something loosely analogous to South Korea's 1%? All of the above? We've already tried, and failed, to figure out exactly why Psy's "Gangnam Style" is so popular and immortal. But having surpassed 4 billion views, Cho Soo-hyun's cheesy horse trot-filled music video has proven it will live on in internet history books for the rest of time. Just accept it.
31. ??? --> Profit!
South Park has given the world its fair share of iconic moments, but nothing sums up the internet era of late capitalism better than this gem, taken from an episode in which underpants gnomes steal underwear in order to turn a profit. How do they make a profit? Through a simple three-phase plan, the second phase of which is simply "?". In time, some form of the punchline "Profit" became a golden response to any example of poor planning or a dumb idea destined for failure.
30. "Thanks, Obama"
No president inspired more online invective and praise than Barack Obama, largely because he was America's first true president of the internet age. "Thanks, Obama" took off as sarcastic right-wing retort to the perceived problems with Obama's health-care bill, but it soon became an even more sarcastic way to blame the president for anything that went wrong in life—no matter how minor. A meme for all political leanings, "Thanks, Obama" has defied constitutionally mandated term limits and continues to govern the meme-verse.
Queen of the Macros, the Ermahgerd meme emerged from an immediate collective understanding that "ermahgerd gersbermps" is exactly how the subject of this macro would pronounce, "Oh my God, Goosebumps." This meme actually influenced the way people speak, turning "ermahgerd" into a stock nerdy response to any object of enthusiasm. Say it out loud and try not to laugh. We can't do this meme more justice than Vanity Fair has already done in its profile of the woman in the photo, so read that.
28. "It's a trap!"
When Admiral Ackbar casually dropped this line in Star Wars: Episode VI–Return of the Jedi, the galaxy quaked. Not because of the ambush. But because fans the universe over were stoked for another everlasting catchphrase, one they'd be able to use years later, as a reaction to pretty much anything remotely sketchy, on one of those crazy, crazy dot com things. (N.B.: Not a tarp.)
27. Roll Safe
In 2016, while playing the character R.S. (aka Roll Safe) in the BBC Three's #HoodDocumentary series, actor Kayode Ewumi made a coded oral sex quip. The screengrab of that moment is now, and forever, your best-worst source of advice.
26. It's Peanut Butter Jelly Time!!!
What time is it? It's Peanut Butter Jelly Time. Before this was a goofy Family Guy gag, this delightfully silly meme started on forums as a piece of Flash animation where a chipper banana dances around as "Peanut Butter Jelly Time," a track from the Buckwheat Boyz, blares in the background. Don't question it. Submit to the banana.
It might surprise you where "derp" came from. It shouldn't. Matt Stone first yelled the word after he was caught licking a mother's vibrating dildo in BASEketball. DERP! The term, which has come to represent palpable moments of failure and stupidity, has since found its way into a number of South Park episodes (remember Mr. Derp?) and, more importantly, mainstream internet vernacular. Thank you, Matt, Trey, and Mr. Zucker.
On May 28, 2016, a toddler climbed into a gorilla enclosure at the Cincinnati Zoo. An employee who feared for the child's life shot and killed the gorilla. The months that followed resurrected Harambe in ways no one could have predicted. In a different era, the incident would have been nothing more than a story in the local newspaper. But we live in the Meme Age, and Harambe became a rallying symbol for a slice of internet entrenched in irony. Harambe got his own Change.org petition. Twitter users placed him alongside the other celebrities so publicly and crudely mourned. Then the nonsensical "Dicks out for Harambe" rallying cry took hold. Then 11,000 people supposedly voted for Harambe in the US presidential election (or was it 15,000? Or was it fake news?!). A Harambe Cheeto sold for $99,900 on eBay. What does it mean? It's only a mirror.
Whether he's sipping tea or talking to an evil doppelgänger, this Muppet has earned a decidedly different identity online. Warm and fuzzy? Nah. Friendly and caring? Not quite. When you see him from behind the keyboard, no longer is he Sesame Street's most famous protagonist. He's a devil for your shoulder, the king of minding his own business—the kind who has the same longevity and malleability as a SpongeBob. Long may he reign.
22. Me IRL/It Me
The internet dishes out a veritable all-you-can-eat buffet of ridiculous humans to mock, so self-deprecation (with some implicit mocking) can be a welcome respite. The phrase "me irl"—"in real life," if you live in a pineapple under the sea and have no idea what that means—got its start way back in 1997, but (as with most memes) blew up on Reddit much later. The closely related "It Me" meme serves the same purpose and is also useful, although it did originate from a highly problematic text exchange.
21. "You're the man now, dog"
No one could have predicted that a mildly problematic line from Sean Connery's earnest drama Finding Forrester would become the emblem for repetitive internet nonsense, but hey, here we are! In 2001, Max Goldberg slapped Connery's line-reading with a tiled mosaic of the man's face and cast it off into an infinite loop. Goldberg would open the site up to user-created "YTMND" memes, both bite-size and epic, mesmerizing and irritating as hell. No other platform could support such gifts as the Master Control Program from TRON singing Katy Perry's "Hot N Cold."
A parody of leet speak? The conversion of adorableness into syntax? Who knows why 4chan users started slapping photos of cute cats with grammar-violating, Z-filled captions, but the meme took off, prompting one genius forum-dweller to start I Can Haz Cheezeburger, a hub for all things feline.
19. Dramatic Prairie Dog (sometimes dba Dramatic Chipmunk)
Animals are hilarious and cute! That's one of the great truths that makes the internet go 'round. Even better when they're making delightfully anthropomorphic faces or behaving in ways that make you believe they're more human (or humans are more animal) than anyone assumed. This prairie dog—erroneously called a chipmunk by many—first appeared in all his dramatic glory on the early 2000s Japanese show Hello Morning! Then YouTube came along, and in 2007 our dramatic hero became a go-to cut whenever a real-life M. Night Shyamalan twist thickened the plot.
18. Smiley face
What could endure longer, with more universality, than the simple smiley face? Of course, it's not so simple—now your everyday speech is peppered with emojis and emoticons of all kinds, but Harvey Ball's 1963 depiction of a yellow circle with two black eyes and an upward grin set the stage for everything from your cry-laugh reactions to wink emoticons... hell, we wouldn't even have an eggplant emoji without the path the yellow smiley face set in motion. It is the ur-text of contemporary communication. Without it, we cease to exist. Or we type out entire text messages, at the very least.
17. Demotivational posters
The cheesy motivational staples of high-school biology classrooms and corporate break rooms cried out for parody, and pretty soon, there were more than enough parodies to go around. It's not totally clear who thought that stock photography and cliches would inspire people to live their best lives, but as always, the power of memes pretty quickly rendered the originals obsolete.
16. Nyan Cat
It takes an army to generate a lasting meme. Fused together from a GIF, designed by a 25-year-old in Dallas, Texas, and a Japanese music video cover of "Nyanyanyanyanyanyanya," Nyan Cat popped up on YouTube in 2011 and changed the way internet kids saw cats with Pop-Tart bodies who fart rainbows forever. The simplicity of Nyan Cat's image made it easy to repurpose with world flags, various cat faces, and unmentionable shit we'll leave you to imagine for yourself.
15. Nigerian prince
You've heard some version of the scam before, which doesn't always involve Nigeria or a prince, but is often called a "419 scam" after its code in Nigerian law: You get an email in stilted English from a stranger telling a tale of death, political difficulties, inheritance, and your kind assistance, with a goal of making you cough up your bank account information. As a scam, it's pretty easily ignored; the modern email version dates all the way back to the Spanish prisoner scam of the late 1700s, so it certainly has durability. As a meme, it's infinitely malleable, providing a ready comeback for anyone you perceive to be gullible ("Oh, you believe politicians have the public's interest at heart? I also know a Nigerian prince you should meet.") or a reference point for clever macros. This one shows no sign of slowing down.
14. Crying Michael Jordan
The rare example of a meme that made its own news in real time, Crying Michael Jordan originated from Jordan's vindictive, self-serving Hall of Fame speech. If you haven't watched the source material, please do. Of the many gifts that speech gave the world (has a father ever told his kids "I wouldn't want to be you" in front of a bigger audience?!), a red-eyed, blubbering meme is surely the greatest. You can put MJ's face on just about anything to lighten the mood, but get your meme feet wet with a Drake album, Mount Rushmore, James Harden's beard, or Donald Trump's trunk.
13. Chuck Norris Facts
Thank god for Conan O'Brien, who in 2004 effectively re-catapulted Chuck Norris back into the zeitgeist with his Walker, Texas Ranger lever. Ever since, the martial artist has become the subject of an almost innumerable number of hilarious alternative facts exaggerating his strength, virility, and badassery. (Seriously, there's a database full of 'em.) Chuck's favorite? "They wanted to put Chuck Norris' face on Mount Rushmore, but the granite wasn't hard enough for his beard."
12. Bert Is Evil
While this breed of subversive fun at the expense of childhood icons seems to be a trademark of today’s internet, Bert Is Evil is a product of the '90s, making it one of the internet’s first true memes. In 1997, designer and Ernie-apologist Dino Ignacio created the parody website of the same name, putting Sesame Street's Bert into incredibly evil situations. The meme was early and influential, gaining massive mainstream media coverage after a picture from the site—featuring Bert with Osama Bin Laden—was mistakenly put on a poster at a rally in Afghanistan. Obviously, the American people were confused at the connection here (ah, the internet was so much simpler back then!). But, with the proper explanation, this might be the moment where many Americans found out what memes are in the first place. At the expense of Bert, of course.
One person's Shiba Inu is another person's meme. Case in point: When a Japanese kindergarten teacher put a picture of a good little doggo online, the internet came with Comic Sans captions, beautifully awful syntax, and a custom payment system. To know doge is to know a very special and everlasting way of life.
10. "U mad bro?"
Nothing infuriates an angry or jealous person more than trivializing his rage or jealousy, which is exactly what the always appropriate response "U mad bro?" accomplishes. The simpler "You mad" first came to prominence when Cam'ron shut down Bill O'Reilly during a segment on whether rap was harmful to children (thank God those days are over... hopefully). Eventually, "u mad?" and "u mad bro?" became go-to quips on bodybuilding forums and during video gameplay whenever anyone got a little salty. Now, it can exist in virtually any format: on political macros, on group texts, in Vines (RIP), as its very own song, on top of other memes... you name it, bro.
9. Keyboard Cat
Boo boo boo boo boo buhboooooo buh. Boo boo boo boo boo buhboooooo buh. The shirt. The eyes. The head raise. ICONIC. This video is, in a few ways, the Mozart of internet videos: playful, passionate, a crown jewel of history. Evolving from a piece of performance art to an exclamation point at the end of fail videos to a mainstream sensation, Fatso the tabby paved the way for dozens of future animal supawstars.
8. Condescending/Sarcastic Willy Wonka
In 1971's Willy Wonka & the Chocolate Factory, when actor Gene Wilder introduces Wonka's most secret machine, he strikes an incredible pose. In the moment, it's a pose of playful suspense, but when screengrabs of the scene began popping up across internet forums the world over, Wilder's pose came to indicate delicious, sarcastic condescension. Which really can apply to anyone or anything posting/posted publicly online.
7. The Dancing Baby
Not only is this gyrating little burst of uncanny valley—by our estimation—the world's first internet meme, it's also one of the critical turning points of internet history, overall. In 1996, Michael Girard designed the baby as a demo to prove motion could be effectively programmed on a computer. When it landed in the lap of a LucasArts designer, he turned it into what was effectively the world's first GIF. From there, it slid into a thousand email inboxes (remember, this was still when snail mail wasn't called snail mail) and became the internet's first true phenomenon, culminating in a recurring role alongside confused '90s lawyer Ally McBeal. The Dancing Baby launched a million GeoCities pages, and remains the pure representation of the internet's infant (hehe) stage.
Andre the Giant is the quintessential example of an ever-evolving meme that crossed from the physical realm to the cold ones and zeroes of the internet. Andre the Giant was the gargantuan professional wrestling star whose size led to wild apocryphal claims, like the story that he drank 156 (or was it 127?) beers in a single sitting, or that he was so large as a child that he couldn't ride the bus, so Nobel laureate Samuel Beckett drove him to school. In 1989, street artist Shepard Fairey created a stencil of Andre the Giant with the added phrase "Has a Posse," and distributed them all over the East Coast, where they became popular in the skater subculture. Eventually, the stencil morphed into a stylized image of Andre the Giant with the word "OBEY" underneath, a kind of vaguely anti-authoritarian message that could be replicated ad infinitum in stickers, street art, online, and on clothing.
5. "All Your Base Are Belong to Us"
This former Flash video, spread primarily on forums like Something Awful at the turn of the Willennium, launched the modern meme format, the Image Macro (image with white block letters over). The phrase itself is a direct quote from a shoddily translated Japanese Sega Genesis game, Zero Wing. Like any meme worth its Salt Bae, "All Your Base" spawned countless remixes and appropriations, as it became shorthand for nerd culture, and was even [cringe] covered in local news broadcasts. Never has someone being horrible at their job been so beneficial to society.
4. "Deal With It" sunglasses
Dang, someone disagreeing with you on the internet? Need someone to just fucking handle something for you? This years-old retort should do the trick. All you need to put them in their place, my friend, is a pair of pixelated sunglasses and these three simple words: Deal. With. It. (Dog optional, but preferable.)
Like Trololo, but English. Just kidding. Rickroll's roots go all the way back to 4chan circa 2007, when users began posting bait-and-switch links that led to Rick Astley's solo single debut instead of duckrolls. A beautifully random trolling phenomenon was born, one that continues today, one that's never gonna give you up, never gonna let you down, never gonna run around and desert you, never gonna make you cry, never gonna say goodbye, never gonna tell a lie and hurt you.
Primitive Sponge. Stunned Patrick. Confused Mr. Krabs. If you needed any more proof that SpongeBob SquarePants was amazing, and not just for kids, all the show's hilarious screengrabs-turned-viral reactions should do the trick. They're versatile, recognizable, and proving immortal.
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Concept that spreads from person to person via the Internet
An Internet meme, more commonly known simply as a meme (MEEM), is an idea, behavior, or style (meme) that is spread via the Internet, often through social media platforms and especially for humorous purposes. What is considered a meme may vary across different communities on the Internet and is subject to change over time. Traditionally, they were a concept or catchphrase, but the concept has since become broader and more multi-faceted, evolving to include more elaborate structures such as challenges, GIFs, videos, and viral sensations.
Internet memes are considered a part of Internet culture. They can spread from person to person via social networks, blogs, direct email, or news sources. Instant communication on the Internet facilitates word of mouth transmission, resulting in fads and sensations that tend to grow rapidly. An example of such a fad is that of planking (lying down in public places); posting a photo of someone planking online brings attention to the fad and allows it to reach many people in little time. The Internet also facilitates the rapid evolution of memes.
One hallmark of Internet memes is the appropriation of a part of broader culture; in particular, many memes use popular culture (especially in image macros of other media), which can sometimes lead to issues with copyright. "Dank" memes have emerged as a new form of image-macros, and many modern memes take on inclusion of surreal, nonsensical, and non-sequitur themes. Colloquially, the terms meme and Internet meme are used more loosely, having become umbrella terms for any piece of quickly-consumed comedic content that may not necessarily be intended to spread or evolve.
There are two central attributes of Internet memes: creative reproduction of materials and intertextuality. Creative reproduction refers to "parodies, remixes, or mashups," and include notable examples such as "Hitler's Downfall Parodies", and "Nyan Cat", among others. Intertextuality may be demonstrated through memes that combine different cultures; for example, a meme may combine United States politician Mitt Romney's assertion of the phrase "binders full of women" from a 2012 US presidential debate with the Korean pop song "Gangnam style" by overlaying the politician's quote onto a frame from Psy's music video where paper blows around him. The intertextuality in the example gives new meaning to the paper blowing around Psy; the meme indexes intertextual practices in political and cultural discourses of two nations.
The spread of Internet memes has been described as occurring via two mechanisms: mimicry and remix. Remix occurs when the original meme is altered in some way, while mimicry occurs when the meme is recreated in a different fashion to the original. The results in the study of Online Memes, Affinities, and Cultural Production, show that the internet directly adds some longevity in a meme's lifespan.
There is no single format that memes must follow. Photographs of people or animals, especially stock photos, can be turned into memes by superimposing text, such as in Overly Attached Girlfriend. Rage comics are a subcategory of memes which depict a series of human emotions and conclude with a satirical punchline; the sources for these memes often come from webcomics. Other memes are purely viral sensations such as in Keyboard Cat.
Evolution and propagation
An Internet meme may stay the same or may evolve over time, by chance or through commentary, imitations, parody, or by incorporating news accounts about itself. Internet memes spread online through influences such as popular culture. In addition, memes can be subjected to in-jokes within online communities such as Twitter, Tumblr, Facebook, YouTube, Reddit, and 4chan. This refers to the memes in-groupness as it communicates an exclusive cultural knowledge unbeknown to general users. In common internet memes, there is a basis for cultural relevance in certain text and imagery associated with memes. On the macro level, internet memes must be encoded and decoded. Through the spreading process, memes invokes studium and punctum memetrics. Punctum is the aesthetic affiliation to a piece of imagery, thus invoking a reaction. It is the affect of the image. In utilizing affect as a visual vernacular, internet memes create a culture of unspoken referential importance. By using explicit cultural knowledge, internet memes provide affect as the emerging communication. Studium is the entertaining aspect of internet memes. With the combination of studium and punctum memetrics, individuals perceive and spread memes from their cultural significance to types of memes.
Consequently, an internet meme can also rapidly become 'unfashionable', losing its humorous qualities to certain audiences, often even most prevalently by its creator(s). Internet memes usually are formed from some social interaction, pop culture reference, or situations people often find themselves in. Their rapid growth and impact has caught the attention of both researchers and industry. Academically, researchers model how they evolve and predict which memes will survive and spread throughout the Web. The phenomena of viral memes is a users to users experience the represents participatory culture on online platforms.
One empirical approach studied meme characteristics and behavior independently from the networks in which they propagated, and reached a set of conclusions concerning successful meme propagation. For example, the study asserted that Internet memes not only compete for viewer attention generally resulting in a shorter life, but also, through user creativity, memes can collaborate with each other and achieve greater survival. Also, paradoxically, an individual meme that experiences a popularity peak significantly higher than its average popularity is not generally expected to survive unless it is unique, whereas a meme with no such popularity peak keeps being used together with other memes and thus has greater survivability.
Multiple opposing studies on media psychology and communication have aimed to characterize and analyze the concept and representations in order to make it accessible for the academic research. Thus, Internet memes can be regarded as a unit of information which replicates via the Internet. This unit can replicate or mutate. This mutation instead of being generational follows more a viral pattern, giving the Internet memes generally a short life. Other theoretical problems with the Internet memes are their behavior, their type of change, and their teleology.
Internet memes have been examined by Dancygier and Vandelanotte in 2017 for aspects of cognitive linguistic and construction grammar. The authors analyzed some selective popular image macros like, Said no one ever, One does not simply, But that's none of my business, and Good Girl Gina to draw attention to the constructionally, multimodality, viewpoint and intersubjectivity of these memes. They further argued that with the combination of text and images, the Internet memes can add to the functioning linguistic construction frame as well as create new linguistic constructions.
Writing for The Washington Post in 2013, Dominic Basulto asserted that with the growth of the Internet and the practices of the marketing and advertising industries, memes have come to transmit fewer snippets of human culture that could survive for centuries as originally envisioned by Dawkins, and instead transmit banality at the expense of big ideas.
Origins and early memes
The word meme was coined by Richard Dawkins in his 1976 book The Selfish Gene as an attempt to explain how ideas replicate, mutate, and evolve (memetics). Emoticons are one of the first resemblances of internet memes. In 1982, Scott E. Fahlman introduced the sideways smiley face formed by punctuation marks, with an intention to create emotion and expressions with the use of digital imagery. The concept of the Internet meme was first proposed by Mike Godwin in the June 1993 issue of Wired. In 2013, Dawkins characterized an Internet meme as being a meme deliberately altered by human creativity—distinguished from biological genes and his own pre-Internet concept of a meme, which involved mutation by random change and spreading through accurate replication as in Darwinian selection. Dawkins explained that Internet memes are thus a "hijacking of the original idea", the very idea of a meme having mutated and evolved in this new direction. Furthermore, Internet memes carry an additional property that ordinary memes do not: Internet memes leave a footprint in the media through which they propagate (for example, social networks) that renders them traceable and analyzable.
Internet memes grew as a concept in the mid-1990s. At the time, memes were just short clips that were shared between people in Usenet forums. As the Internet evolved, so did memes. When YouTube was released in 2005, video memes became popular. Around this time, rickrolling became popular and the link to this video was sent around via email or other messaging sites. Video sharing also created memes such as "Turn Down for What" and the "Harlem Shake". As social media websites such as Twitter and Facebook started appearing, it was now easy to share GIFs and image macros to a large audience. Meme generator websites were created to let users create their own memes out of existing templates. Memes during this time could remain popular for a long time, from a few months to a decade, which contrasts with the fast lifespan of modern memes. Over the years, many memes have originated on the 4chan website, which have been described as "the cradle of memes, trolling and alterculture"; major memes popularized by that site include lolcats as well as the pedobear.: 74
Early in the Internet's history, memes were primarily spread via email or Usenet discussion communities. Messageboards and newsgroups were also popular because they allowed a simple method for people to share information or memes with a diverse population of Internet users in a short period. They encourage communication between people, and thus between meme sets, that do not normally come in contact. Furthermore, they actively promote meme-sharing within the messageboard or newsgroup population by asking for feedback, comments, opinions, etc. This format is what gave rise to early Internet memes, like the Hampster Dance. Another factor in the increased meme transmission observed over the Internet is its interactive nature. Print matter, radio, and television are all essentially passive experiences requiring the reader, listener, or viewer to perform all necessary cognitive processing; in contrast, the social nature of the Internet allows phenomena to propagate more readily. Many phenomena are also spread via web search engines, Internet forums, social networking services, social news sites, and video hosting services. Much of the Internet's ability to spread information is assisted from results found through search engines, which can allow users to find memes even with obscure information.
The earlier forms of image based memes include the demotivator, image macro, photoshopped image, LOLCats, advice animal, and comic. The Demotivator image includes a black background with white, capitalized text, often in Times New Roman. The objective of using this format was to parodize inspirational and motivational posters, where the name "demotivator" is derived from. Image macro consists of an image with white Impact font within a black border. The text/context of the meme is at the top and bottom of the image itself. The photoshopped image is closely related to the macro image, but often is created without the use of text, mostly edited with another image. Advice animals contain a photoshopped image of an animal's head on top of a rainbow/color wheel background. It includes the image macro of the top and bottom text with Impact font. LOLCats incorporate the design of image macro and advice animals, but instead of just the cat's head, it is the entire picture unedited with top and bottom text, often with the usage of Internet slang. Comics follow a typical newspaper comic strip format; there are a variety of different ways to create one, as multiple images and texts can be used to create the overall meme. Rage comics such as Trollface were often used to create comic memes.
Modern memes can generally be described as more visually (rather than contextually) humorous, absurd, niche, diverse and self-referential than earlier forms. As a result, they are less intuitive and are less likely to be fully understood by a wider audience. By the mid-2010s, they began to arise first in the form of "dank" memes, a sub-genre of memes usually involving meme formats in a different way to the image macros that were in large use before. The term "dank", which means "a cold, damp place", was later adapted by marijuana smokers to refer to high-quality marijuana, and then became an ironic term for a type of meme, also becoming synonymous for "cool". This term originally meant a meme that was significantly different from the norm but is now used mainly to differentiate these modern types of memes from other, older types such as image macros. Dank memes can also refer to those which are "exceptionally unique or odd". They have been described as "Internet in-jokes" that are "so played out that they become funny again" or are "so nonsensical that they are hilarious".
The formats are usually from popular television shows, movies, or video games and users then add humorous text and images over it. The culture surrounding memes, especially dank memes, grew to the point of the creation of many subcultures surrounding them. For instance, a "meme market", satirizing on the kind of talks and stocks found normally on Wall Street, was created in September 2016. Originally started on Reddit as r/MemeEconomy, people would only jokingly "buy" or "sell" shares in a meme to indicate how popular a meme was thought to be. The market is seen as a way to show how people assign value to commonplace and otherwise valueless things such as memes.
One example of a dank meme is "Who Killed Hannibal", which is made of two frames from a 2013 episode of The Eric Andre Show. The meme features the host Andre shooting his co-host Buress in the first frame and then lamenting that his co-host has been shot in the next, with Andre often depicted blaming someone else for the shot. This was then adapted to other situations, such as baby boomers blaming millennials for problems that they allegedly caused.
Dank memes also stem from interesting real-life images that are shared or remixed many times. So-called "moth" memes (often stylized as "möth") came about after a Reddit user posted a close up picture of a moth that they had found outside their window onto the r/creepy subreddit. The image became popular and began to be used in memes; according to Chris Grinter, a lepidopterist from the California Academy of Sciences, moth memes gained recognition because of the inexplicability surrounding moths' attraction to lamps.
Irony and absurdism
Many modern memes stem from nonsense or otherwise unrelated phrases that are repeated and placed onto other formats. One example of this is "they did surgery on a grape," from a video of a da Vinci Surgical System performing test surgery on a grape. People sharing the post tended to add the same caption to it ("they did surgery on a grape"), and eventually created a satirical image with several layers of captions on it. Memes such as this one continue to propagate as people start to include the phrase in different, otherwise unrelated memes.
The increasing trend towards irony in meme culture has resulted in absurdist memes not unlike postmodern art. Many Internet memes have several layers of meaning built off of other memes, not being understandable unless the viewer has seen all previous memes. "Deep-fried" memes, memes that have been distorted and run through several filters and/or layers of Lossy compression, are often strange to one not familiar with them. An example of these memes is the "E" meme, a picture of Markiplier photoshopped onto Lord Farquaad from the film Shrek, photoshopped into a scene from Mark Zuckerberg's hearing in Congress.
"Surreal" memes are based on the idea of increasing layers of irony so that they are not understandable by popular culture or corporations. This strange irony was discussed in the Washington Post article "Why is millennial humor so weird?" to show the disconnect from how millennials and other generations conceive of humor; the article itself also became a meme where people photoshopped examples of deep-fried and surreal memes onto the article to make fun of the point of the article and the abstraction of meme culture. Bogna M. Konior has described some memes as "surreal, fatalistic, and apocalyptic." Konior claims this trend is the result of grappling with insurmountable-seeming problems facing modern society, including social inequality and climate change and "the insufficiency of politics at this moment of perceived crisis."
See also: Vine (service) and TikTok
After the success of the application Vine, a format of memes emerged in the form of short videos and scripted sketches. Vine, in spite of its closure in early 2017, has still retained relevance through uploads of viral vines in compilations onto other sharing social media sites such as Twitter and YouTube. Since Vine's shutdown, the service TikTok has been described as a better version of Vine and many comparisons have been made between the two platforms; also based on the upload of short-form videos, TikTok, however, allows videos and memes up to three minutes in length rather than six seconds.
See also: Reaction video
The short-form videos created on sites like Vine and TikTok found use in being posted on other social media sites, such as Twitter, as a form of reacting and responding to other posts. These videos become replicated into other contexts and often become part of Internet culture. An example of a TikTok meme is the cosplay by Nyannyancosplay juxtaposed to the musical track "Mia Khalifa" by iLoveFriday. This meme became known as Hit or Miss. Hit or Miss has been referenced multiple times, including PewDiePie's 2018 Rewind as one of the most influential memes of the year alongside numerous other influential memes of the year. PewDiePie's 2018 rewind video has been viewed over 70 million times and has 8.9 million likes as of April 28, 2020. Hit or Miss has been remixed as well, including by other social media influencers such as Belle Delphine. SirKibbs' YouTube has uploaded a video of Belle Delphine and Kat (Nyannyancosplay) side-by-side comparison and has garnered 2.7 million views as of April 28, 2020.
Public relations, advertising, and marketing professionals have embraced Internet memes as a form of viral marketing and guerrilla marketing to create marketing "buzz" for their product or service. The practice of using memes to market products or services is known as memetic marketing. Internet memes are seen as cost-effective, and because they are a (sometimes self-conscious) fad, they are therefore used as a way to create an image of awareness or trendiness. To this end, businesses have taken to attempting two methods of using memes to increase publicity and sales of their company; either creating a meme or attempting to adapt or perpetuate an existing one. Examples of memetic marketing include the FreeCreditReport.com singing ad campaign, the "Nope, Chuck Testa" meme from an advertisement for taxidermist Chuck Testa, Wilford Brimley saying "Diabeetus" from Liberty Medical and the Dumb Ways to Die public announcement ad campaign by Metro Trains Melbourne.
Marketers, for example, use Internet memes to create interest in films that would otherwise not generate positive publicity among critics. The 2006 film Snakes on a Plane generated much publicity via this method. Used in the context of public relations, the term would be more of an advertising buzzword than a proper Internet meme, although there is still an implication that the interest in the content is for purposes of trivia, ephemera, or frivolity rather than straightforward advertising and news.
Brands' use of memes has disadvantages when considering people's perception of a brand. While effective use of a meme can lead to increased sales and attention, seemingly forced, unoriginal, or unfunny usage of memes can negatively impact the brand as a whole. For instance, the fast food company Wendy's began a social media approach in 2017 that heavily featured memes and was initially met with success, resulting in an almost 50% profit growth that year; however, the strategy has also backfired when sharing memes that are controversial or otherwise negatively perceived by consumers.
Throughout the years, there have been media that used, were inspired by, or centered around various memes. The most popular is Slender Man, a creepypasta meme that have been used in video games, films, and documentaries. Another example is the pop culture novelOtaku Girl that used memes in its story, oftentimes as characters or antagonists, like Ultra-Instinct Shaggy and Big Chungus.
Internet memes are a medium for communicating comical images and or phrases for mass online audiences. As internet memes become a common means of online expression, they become quickly used by those seeking to express political opinions or to actively campaign for (or against) a political entity. In some ways, they can be seen as a modern form of the political cartoon, offering up a way to democratize political commentary.
Early examples of political memes can be seen from those resulting from the Dean Scream. Another example can be seen from MyDavidCameron.com, a website that allowed users to change the text of a British Conservative election campaign poster featuring David Cameron from the 2010 general election. This website was often used to produce memes that replaced the original slogan with a series of exaggerated claims or sarcastic fake campaign promises along with derision of David Cameron's airbrushed appearance.
Within each subsequent election, and the growing importance of visual communications due to the Internet and social media, memes have become a more important element within political campaigns as fringe communities have shaped broader discourse through the use of Internet memes. For example, Ted Cruz's 2016 Republican presidential bid was damaged by Internet memes that speculated he was the Zodiac Killer.
Another internet meme was created from the 2012 US presidential debate surrounding United States politician Mitt Romney's usage of the phrase "binders full of women". Internet meme creators quickly created "My Binders Full of Women Exploded", referencing the Korean pop song "Gangnam style" by overlaying the politician's quote onto a frame from Psy's music video where paper blows around him. This internet meme specifically indexes the central attribute of intertextuality by blending together pop culture with politics.
There has further been academic research that provides evidence that the use of memes during elections has a role to play in informing the public. In a study of 378 Internet memes posted across Facebook during the 2017 general election, McLoughlin and Southern found memes were a widely shared conduit for basic political information to audiences who often did not seek it out. Indeed, a fifth of all political memes posted during the election referenced a political policy which was part of a political parties mandate, while messages promoting people to vote were shared more than 160,000 times, suggesting memes have a small role to play in increasing voter turnout. Satirical memes that express political opinions are effective in not only informing others but also driving political debate and engagement with politics by offering an easy and even fun way to talk about important issues.
Some political campaigns have begun to explicitly taken advantage of the increasing influence of memes; as part of the 2020 US presidential campaign, Michael Bloomberg sponsored a number of Instagram accounts with over 60 million collective followers to post memes related to the Bloomberg campaign. Similar to criticisms against corporations who use meme marketing, the campaign was faulted for treating meme culture as an advertisement or something that can be bought.
The 2020 Presidential Campaign of Kanye West quickly became a meme, following its announcement on Twitter, with numerous celebrities and influencers endorsing the rapper out of irony. Other personalities began announcing their own satirical presidential campaigns, parodying West.
Internet memes provide significant contributions toward social issues. Memetric structures have enabled social movements to become spreadable pieces of information.
During the 2010 It Gets Better Project for LGTBQ+ empowerment, memes were continuously used to promote and uplift LGTBQ+ youth. The Human Rights Campaign equal rights symbol became an internet meme in defending the legalization of same sex marriage.
The Ice Bucket Challenge became a viral meme in promoting and raising money and awareness for amyotrophic lateral sclerosis.
The Occupy Wall Street (OWS) protest movement saw a rise in internet memes after gaining attention on social media. All internet memes that were created and shared during the movement were very important in mediated discussions surrounding the OWS. Typical phrases such as "We Are the 99%" and "This is what democracy looks like", were remixed into memes and subsequently posted in the discussion board of OWS on popular social media sites such as Reddit, Tumblr, and 4chan. Those who actively participated in the movement conversed through these visuals.
Memes making political or social points are sometimes structured as ostensible thought experiments in various forms, such as, "What if A were B in situation X?" and are framed to provoke a particular response. The conclusions intended, however, do not necessarily follow since there can be multiple factors determining the outcomes in situation X.
Internet memes have also been used in the context of religion.
The eligibility of any memes to get copyright protection depends on the copyright law of the country in which such protection is sought. Some of the most popular formats of memes include cinematographic stills, personal or stock photographs, rage comics, and illustrations meant to be a meme, and the copyright implications differ for each of these different formats. There is precedent both for memes to be in violation of copyright and in other memes having copyrights of their own.
If it is found that the meme has made use of a copyrighted work, such as the movie still or photograph without due permission from the original owner, it would amount to copyright infringement. Rage comics and memes created for the sole purpose of becoming memes would normally be original works of the creator and therefore, the question of infringing other copyright work does not arise. In a cinematographic still, part of the entire end product is taken out of context and presented solely for its face value. The still is generally accompanied by a superimposed text of which conveys a distinctive idea or comment, such as the Boromir meme or "Gru's Plan". This does not mean that all memes made from movie still or photographs are infringing copyright. There are defenses available for such use in various jurisdictions which could exempt the meme from attracting liability for the infringement.
Main article: Copyright § Obtaining protection
Under United States copyright law, a creation receives copyright protection if it satisfies four conditions under 17 U.S.C. § 102. For a meme to get copyright protection, it would have to satisfy four conditions:
- It falls under one of the categories of work which is protected under the law
- It is an "expression"
- It has a modest amount of creativity
- It is "fixed".
Memes can be considered pictorial, graphical or motion picture, and so are subject to copyright law. As such, memes are protected under copyright under the same conditions as these mediums, including concepts such as the low threshold of originality for what constitutes creativity (as demonstrated by Feist Publications, Inc., v. Rural Telephone Service Co). Since a meme is essentially a comment, satire, ridicule or expression of an emotion it constitutes the expression of an idea. Memes are contained in the medium of the Internet and so are fixed expressions by 17 U.S.C. § 101.
Main article: Fair use
Fair use is a defense under US Copyright Law which protects work that has made using other copyrighted works. The section provides that if a copyrighted work is reproduced "for purposes such as criticism, comment, news reporting, teaching [...], scholarship or research", it would not amount to infringement. Notably, for memes, the use of the term "such as" in the section denotes that the list is not exhaustive but merely illustrative. Furthermore, the factors mentioned in the section are subjective in nature and the weight of each factor varies on a case to case basis.
The four factors are:
- The purpose or character of use,
- The nature of the copyrighted work,
- The amount and substantiality of the portion used, and
- Effect on the market.
Many memes are transformative in nature as they have no relation to the original work and the motive behind the communication of the meme is personal, in terms of disseminating humor to the public; such memes, being transformative, would be covered by fair use. However, copying memes that are made for the sole purpose of being memes would not enjoy this protection as there is no transformation—the copying has the same purpose as the original meme which is to communicate humorous or entertaining anecdotes. Purpose and character of use weigh in against memes which have been used for commercial purposes because in those cases, the work has not been created for the communication of humor but for economic gain. For example, Grumpy Cat won $710,001 in a copyright lawsuit against the beverage company Grenade which used the Grumpy Cat image on its roasted coffee line and t-shirts.
The nature of the copyrighted work asks what the differences between the meme and the other material are. This factor applies to many types of memes because the original work is an artistic creation that has been published and thus the latter enjoys protection under copyright which the memes are violating. However, as memes are transformative, this factor does not have much weight.
The amount and substantiality of the portion used tests not only the quantity of the work copied but the quality that is copied as well. Memes copy only a small portion of a complete film, whereas for rage comics and personal photographs, the entire portion has been used to create the meme. Despite this, all categories of memes could fall under fair use because the text that is added to those images adds value, without which it would just be pictures. Moreover, the heart of the work is not affected because the still/picture is taken out of context and portrays something entirely different from what the image originally wanted to depict.
Lastly, the effect on the market offers court analysis on whether the meme would cause harm to the actual market of the original copyright work and also the harm it could cause to the potential market. The target audience for the original work and meme is entirely different as the latter is taken out of the context of the original and created for use and dissemination on social media. Rage comics and memes created for the purpose of being memes are an exception to this because the target audience for both is the same and copied work could infringe on the potential market of the original. Warner Brothers was sued for infringing the Nyan Cat meme by using it in its game Scribblenauts.
Some subjects of memes made money from them through licensing deals. In 2021, in a new version of this concept several subjects of memes sold NFTs through auctions. Ben Lashes, who managed numerous memes, said sales of these as NFTs had made $2 million and established memes as serious art. One example of how this idea works is the case of "Disaster Girl", based on a photo of Zoe Roth at age 4 taken in Mebane, North Carolina in January 2005. After the photo became famous and was used hundreds of times without permission, Roth decided to sell the original copy as an NFT, for the equivalent of US$486,716. This allowed the family control over the image's distribution and gave them copyright and 10 percent of proceeds when the NFT was sold.
Under Section 2(c) of the Indian Copyright Act, 1957, a meme could be classified as an 'artistic work' which states that an artistic work includes painting, sculpture, drawing (including a diagram, map, chart or plan), an engraving or a photograph, whether or not any such work possesses artistic quality. The section uses the phrase "whether or not possessing artistic quality", the memes that are rage comics or those such as Keyboard Cat would enjoy protection as they are original creations in the form a painting, drawing, photograph or short video clip, despite not having artistic quality. Memes that made from cinematograph still or photographs, the original image in the background for the meme would also be protected as the picture or the still from the series/movie is an 'artistic work'. These memes are a modification of that already existing artistic work with some little amount of creativity and therefore, they would also enjoy copyright protection.
Main article: Fair dealing
India follows a fair dealing approach as an exception to copyright infringement under Section 52(1)(a) for the purposes of private or personal use, criticism or review. The analysis requires three steps: the amount and substantiality of dealing, the purpose of copying, and the effect on potential markets.
The amount of sustainability of dealing asks about how much of the original work is used in the meme, or how the meme transforms the original content. A meme makes use to existing copyright work whether it is a cinematograph still, rage comic, personal photograph or a meme made for the purpose of being a meme. However, since a meme is made for comedic purposes, taken out of context of the original work, they are transforming the work and creating a new work.
The purpose of copying factors in the purpose of the meme compared to the purpose of the original work. Under Section 52(1)(a), the purpose is restricted to criticism or review. A meme, as long as it is a parody or a criticism of the original work would be protected under the exception, but once an element of commercialization comes in, they would no longer be exempted and because the purpose no longer falls under the those mentioned in the section . When the Indian comedic group All India Bakchod (AIB) parodied Game of Thrones through a series of memes, the primary purpose was to advertise products of companies that have endorsed the group and thus was not fair dealing.
Memes generally do not have an effect on the potential market for a work. There must be no intention on part of the infringer to compete with the original owner of the work and derive profits from it. Since memes are generally meant for comedic value and have no intention to supplant the market of the original creator, they fall within the ambit of this section.
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