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VOX-Pol Blog post.

VOX-Pol Blog Post.

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 td has a higher success rate in pushing them to other communities.

From its start, the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has been successfully exploiting social media networks, most notoriously Twitter, to promote its propaganda and recruit new members, resulting in thousands of social media users adopting pro-ISIS stance every year. Automatic identification of pro-ISIS users on social media has, thus, become the centre of interest for various governmental and research organisations. In this paper we propose a semantic-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual representation of the content published by Twitter users, our approach extracts and makes use of the underlying semantics of words exhibited by these users to identify their pro/anti-ISIS stances. Our results show that classifiers trained from words’ semantics outperform those trained from lexical and network features by 2% on average F1-measure.

Compared to the more spectacular elements of its media repertoire—the slick recruitment campaigns on social media, the artfully composed battlefield footage, the grisly executions—I.S.I.S.’s infographics may seem dull, even trivial. Indeed, these data visualizations have gone largely unremarked, eliciting more bemusement than serious consideration. Against the tendency to discount these images, however, I argue that when I.S.I.S. turns toward charts and diagrams to represent its operations, it launches a stealthy but substantial epistemological challenge to media outlets that depict it as backward and irrational and rely on command of information as an index of Western power. Comparing infographics produced about I.S.I.S. and those produced by the group, I demonstrate that, despite their obvious differences, both types of infographics evince common preoccupations. Like Western news sources, I.S.I.S. creates infographics to map attacks, plot territorial gains, tally and categorize casualties, and track the types of weapons deployed. News media and I.S.I.S. infographics diverge primarily in their affective resonance, as similar information signifies in radically different ways. Ultimately, by producing and circulating these infographics, I.S.I.S. renders simultaneously renders itself more and less intelligible to outsiders: encapsulating its story while confounding prevailing representations as it weaponizes information.

VOX-Pol Blog post.