Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board ( pol ), and Gab, over the course of 13 months. We group the images posted on fringe Web communities ( pol , Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that pol substantially influences the meme ecosystem with the number of memes it produces, while has a higher success rate in pushing them to other communities.
Popularity of Memes. @cite_67 @cite_29 study the popularity of memes spreading as hashtags on Twitter. They model virality using an agent-based approach, taking into account that users have a limited capacity in receiving viewing memes on Twitter. They study the features that make memes popular, finding that those based on network community structures are strong indicators of popularity. Tsur and Rappoport @cite_42 predict popularity of text-based memes on Twitter using linguistic characteristics as well as cognitive and domain features. @cite_12 study memes propagating via text, images, audio, and video on the Yahoo! Meme platform (a platform discontinued in 2012), aiming to predict virality and select memes to be shown to users after login. Coscia @cite_22 studies meme images and distinguishes the traits that make them more likely to be popular on the Web, presenting a case study on the Quickmeme generator site.
@cite_67 @cite_22 @cite_29 @cite_42 @cite_12
2072606289 2171821238 1780764807 915468734 2168984636
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. The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a "meme". Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful. We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes. Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags. Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a “meme” of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.