On the Origins of Memes by Means of Fringe Web Communities (1805.12512v3)

Abstract: 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.

On the Origins of Memes by Means of Fringe Web Communities

This comprehensive paper provides an analytical approach to understanding the propagation and influence of Internet memes across various web communities, with a particular focus on fringe communities known for their unique cultural expressions. Through a meticulous methodology leveraging perceptual hashing and clustering techniques, the authors investigate a large dataset consisting of 160 million images drawn from 2.6 billion posts on platforms like Twitter, Reddit, 4chan's Politically Incorrect Board (/pol/), and Gab, over a 13-month period.

The paper begins by acknowledging the diverse nature of memes as cultural entities that replicate, evolve, and respond to social pressures. The analysis reveals significant findings about the origins and dissemination paths of memes, particularly those with political and racist undertones originating from fringe communities. Notably, /pol/ and The_Donald subreddit on Reddit are identified as significant sources in the meme ecosystem, creating and exporting a considerable number of memes to broader platforms like Twitter and Reddit.

Key findings highlight the supremacy of /pol/ in generating meme content, while The_Donald exhibits high efficacy in distributing memes across mainstream and fringe communities. This dichotomy emphasizes the role of fringe communities as incubators of meme innovation before broader adoption. The pipeline developed in this paper offers a robust tool for large-scale meme ecosystem measurements, allowing for computational efficiency in processing and detecting meme clusters.

The authors employ Hawkes processes to model and quantify the interactions between these web communities, providing insights into the dynamics of meme spreading beyond mere descriptive statistics. This probabilistic model effectively captures the temporal and causal relationships among meme postings across different platforms, illustrating the ripple effects from initial posts in fringe communities to the broader meme landscape.

Practically, the findings reflect on the potential for memes as tools of social manipulation, aligning with previous media speculation about their wielding in political contexts to sway public opinion. The characterization of racism-related and politics-related memes through the lens of virality and influence underscores the complex interplay between cultural production and digital dissemination paths.

The research critically advances theoretical understanding by quantifying meme influence and propagation, while also posing significant implications for the paper of digital misinformation, societal influence, and cultural anthropology. Speculation on future developments highlights evolving challenges in maintaining informational health on social platforms, emphasizing the need for tools to identify and manage potentially harmful content.

Overall, this paper provides a foundational piece for further academic inquiry into digital culture and presents a framework that can be adapted to other contexts and data forms, rendering it a significant contribution to computational social science and digital media studies.