Contextualizing Internet Memes Across Social Media Platforms
This addresses the need for better understanding and monitoring of memes for researchers and platform moderators, but it is incremental as it builds on existing knowledge graphs and similarity methods.
The paper tackles the problem of holistically tracking and identifying internet memes across social media platforms by using a knowledge graph (IMKG) to contextualize them, resulting in a framework that maps memes from Reddit and Discord to IMKG to study their prevalence and provide context.
Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG -- a recently released knowledge graph of internet memes. We leverage this grounding to highlight the potential of our proposed framework to study the prevalence of memes on different platforms, map them to IMKG, and provide context about memes on social media.