On the Origins of Memes by Means of Fringe Web Communities

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.Comment: A shorter version of this paper appears in the Proceedings of 18th ACM Internet Measurement Conference (IMC 2018). This is the full versio

Multimodal and Explainable Internet Meme Classification

Warning: this paper contains content that may be offensive or upsetting. In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods

4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community

We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b/ succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b/ and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90% of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b/ participants use to establish status and frame their interaction

An ecological perspective on the use of memes for language learning

Internet memes—usually taking the form of an image, GIF, or video with text—have become an important type of semiotic tool for meaning making. Due to the fact that memes can help learners leverage semiotic modes in social contexts, they hold great potential for language education. Integrating ecological social semiotic frameworks, this comparative case study examined the semiotic affordances of using memes for language learning in the digital wilds, with a focus on self-identified highly-motivated learner-memers in a university-level student-run Chinese-English intercultural chat group. Data sources included meme artifacts, screen shots, and recordings of meme-related communicative practices as well as semi-structed interviews with each participant. Analysis suggests there were four affordances perceived and utilized by the participants, including linking learners to emergent semiotic repertoires, L2 user agency, increased motivation, and personhood development. Key to learners’ experiences was their awareness of perceived semiotic affordances and their agency to participate in meaning making for potentially meaningful learning experiences. We conclude with pedagogical implications for integrating the rich semiotic resources of memes into language classrooms