MultiHub Forum

I'm a digital media student working on my thesis about the lifecycle and mutation of internet memes, specifically how they evolve as they cross between different online subcultures and platforms. I'm trying to track specific examples, like a particular format that started on a niche forum, got popular on Twitter, and then was co-opted by brands on Instagram, but I'm struggling with methodology for capturing and analyzing these shifts in meaning and context at scale. For researchers or avid cultural observers, what tools or frameworks have you found useful for mapping the trajectory and semantic changes of a viral meme, and how do you account for the loss of original context or inside jokes when they hit the mainstream?

Nice topic. A practical way to study this is to treat a meme as a vector of features (visual template, caption style, and shared jokes). Build a data pipeline across platforms: Reddit (cross-posts and subreddits), Twitter/X, TikTok, Instagram, YouTube comments. For each candidate meme, collect around 12–24 months of data if possible. Use CLIP to embed image-caption pairs and cluster variants; use BERTopic to capture semantic drift in captions; align with a timeline of platform contexts. Then measure drift with cosine similarity of top keywords over rolling windows; supplement with Hawkes-process-based diffusion to estimate cross-platform inflows. Validate by hand on a sample of clusters to ensure you’re not mis-classifying.