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In the world of memes, that trouble is coming in the form of lawsuits. Meme creators have begun suing people for copyright infringement for using their memes without permission. Last November, a jury from Sioux City, Iowa, determined that a politician named Steve King broke the law when he used the Success Kid meme in a reelection fundraising campaign. Laney Griner, the mother of the child who became known as Success Kid and the person who snapped the iconic photo back in 2007, also filed another lawsuit against a fireworks company that used the image of her son to advertise one of its products. The fireworks company settled with Griner for an unspecified amount.

Just about everyone sends an unending volley of memes to each other, and posts them to their social media channels. So does that mean that everyone needs to be as worried as Scared Hamster about getting sued?

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A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows.

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

In Fig. 2 we compare the daily values of the system entropy to the corresponding average user entropy. The key observation here is that a user's breadth of attention remains essentially constant irrespective of system diversity. This is a clear indication that the diversity of memes to which a user can pay attention is bound. With the continuous injection of new memes, this indirectly suggests that memes survive at the expense of others. We explicitly assume this in the information diffusion model presented later.

Fig. 3 shows that users are more likely to retweet memes about which they posted in the past (Pearson correlation coefficient = 0.98). This suggests that memory is an important ingredient for a model of meme competition and we explicitly take this aspect into account in the model presented below.

The social network underlying the meme diffusion process is a critical component of the model. To obtain a network of manageable size while preserving the structure of the actual social network, we sampled a directed graph with 105 nodes from the Twitter follower network (details in Methods). The nodes correspond to a subset of the users who generated the posts in our empirical data. To evaluate the predictions of our model, we compare them with empirical data that includes only the retweets of the same subset of users. To study the role played by the network structure in the meme diffusion process, we also simulated the model on a random Erds-Rnyi (ER) network with the same number of nodes and edges. As shown in Fig. 6, the model captures the main features of the empirical distributions of meme lifetime and popularity, user activity and breadth of user attention. The comparison with the corresponding distributions generated using the ER network shows that in general, the heterogeneity of the observed quantities is greatly reduced when memes spread on a random network. This is not unexpected. Consider for example meme popularity (Fig. 6(b)); the real social network has a broad (scale free, not shown) distribution of degree, with a consistent number of hub users who have a large number of followers. Memes spread by these users are likely to achieve greater popularity. This does not happen in the ER network where the degree distribution is narrow (Poissonian). The difference observed in the distribution of breadth of user attention, for both low and high entropy values (Fig. 6(d)), may be explained by the heterogeneity in the number of friends. Users with few friends may have low breadth of attention while those with many friends are exposed to many memes and thus may exhibit greater entropy.

The second key ingredient of our model is the competition among memes for limited user attention. To evaluate the role of such a competition on the meme diffusion process, we simulated variations of the model with stronger or weaker competition. This was accomplished by tuning the length tw of the time window in which posts are retained in an agent's screen or memory. A shorter time window (tw < 1) leads to less attention and thus increased competition, while a longer time window (tw > 1) allows for attention to more memes and thus less competition. As we can observe in Fig. 7, stronger competition (tw = 0.1) fails to reproduce the large observed number of long-lived memes (Fig. 7(a)). Weaker competition (tw = 5), on the other hand, cannot generate extremely popular memes (Fig. 7(b)) nor extremely active users (Fig. 7(c)).

We also simulated our model without user interests, by setting pm = 0. The most noticeable difference in this case is the lack of highly focused individuals. Users have no memory of their past behavior and can only pay attention to memes from their friends. As a result, the model fails to account for low entropy individuals (not shown but similar to the random network case in Fig. 6(d)).

Our results do not constitute a proof that exogenous features, like intrinsic values of memes, play no role in determining their popularity. However we have shown that at the statistical level it is not necessary to invoke external explanations for the observed global dynamics of memes. This appears as an arresting conclusion that makes information epidemics quite different from the basic modeling and conceptual framework of biological epidemics. While the intrinsic features of viruses and their adaptation to hosts are extremely relevant in determining the winning strains, in the information world the limited time and attention of human behavior are sufficient to generate a complex information landscape and define a wide range of different meme spreading patterns. This calls for a major revision of many concepts commonly used in the modeling and characterization of meme diffusion and opens the path to different frameworks for the analysis of competition among ideas and strategies for the optimization/suppression of their spread.

Today, memes have a specific connotation in our digital environment. What makes memes so special is their way of communicating attitudes, feelings and situations. Because of their popularity, it comes as no surprise that brands want a piece of this pie.

Did you know that millennials spend over 200 minutes online every day? Memes are so prolific that there's a good chance millennials and Gen Zers are laughing at and sharing memes while online. This gives brands plenty of opportunities to engage with their audience.

I have been telling people memes are the future of social marketing for years -- I would always get pushback," said Razvan Romanescu, the co-founder of Memes.com. "Not anymore. The times have changed and every brand is now adapting ..."

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Our results open up future research directions for studying memes in a comprehensive fashion, which could lead to new insights in fields as disparate as cultural evolution, innovation, information diffusion, and social media.

Citation networks of the Web of Science and the American Physical Society (APS) data sets reveal community structures that nicely align with scientific disciplines, journals covering particular subfields, and occurrences of memes. The generation of the visualizations is based on Gephi [31] and the OpenOrd plug-in [30], which implements a force-directed layout algorithm that is able to handle very large graphs.

Time history of top physics memes based on their meme scores obtained from the American Physical Society data set. The time axis is scaled by publication count. Bars and labels are shown for all memes that top the rankings for at least 10 out of the displayed 911 points in time. The gray area represents the second-ranked meme at a given time.

Use of the terms "viral" and "memes" by those in the marketing, advertising and media industries may be creating more confusion than clarity. Both these terms rely on a biological metaphor to explain the way media content moves through cultures, a metaphor that confuses the actual power relations between producers, properties, brands, and consumers. Definitions of 'viral' media suffer from being both too limiting and too all-encompassing. The term has 'viral' has been used to describe so many related but ultimately distinct practices -- ranging from Word-of-Mouth marketing to video mash-ups and remixes posted to YouTube -- that just what counts as viral is unclear. It is invoked in discussions about buzz marketing and building brand recognition while also popping up in discussions about guerilla marketing, exploiting social networks, and mobilizing consumers and distributors. Needless, the concept of viral distribution is useful for understanding the emergence of a spreadable media landscape. Ultimately, however, viral media is a flawed way to think about distributing content through informal or adhoc networks of consumers. e24fc04721