On the Origins of Memes by Means of Fringe Web Communities

By Savvas Zannettou, T. Caulfield, Jeremy Blackburn, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, Guillermo Suarez-Tangil. ACM/SIGCOMM Internet Measurement Conference, 2018

301 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Cultural Studies, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1e06557ceacfc6c95eed48dc77d0ebd62443e14b

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Citations in the corpus

Spread of Hate Speech in Online Social Media (2018, methodology): “In order to understand the diffusion dynamics in Gab, we collect a massive dataset of posts and users by following the crawling methodology in (Zannettou et al. 2018a).”
Finding and Characterizing Information Warfare Campaigns (2020, methodology): “[264] that clusters image streams based on pHash and identifies memetic clusters using meme annotation from sites such as “Know Your Meme”.”
Auditing radicalization pathways on YouTube (2019, background): “, /r/TheDonald) have great influence over which memes [43] and news [44] are shared in large social networks, such as Twitter.”
The Pushshift Reddit Dataset (2020, methodology): “[117] Pushshift data has already been used to understand the phenomenon of hate speech and political extremism [79, 25, 43, 44, 64] and trolling and negative behaviors in fringe online spaces [4, 125, 126].”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “Although some work has addressed the task of detecting hateful images [118, 121], this lead to the creation of a publically available labelled training dataset in only one case [51].”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “Although some work has addressed the task of detecting hateful images [75, 77, 78], this has not led to publicly available datasets.”
Challenges and frontiers in abusive content detection (2019, background): “One noticeable exception is research by Zannettou et al. who create a system for detecting hateful memes by mining hateful Internet forums (Zannettou, Caulfield, Blackburn, & Cristofaro, 2018).”
Why So Toxic?: Measuring and Triggering Toxic Behavior in Open-Domain Chatbots (2022, methodology): “We do so as datasets from these platforms have been used widely in previous work focusing on offensive speech [40, 74].”