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.
Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings. Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media. 