Sure thing. I’d start by scraping the top meme‑heavy subreddits and Twitter threads, then run a time‑series analysis on the upvote and share rates. Add sentiment scoring to detect when a meme’s tone shifts from niche to universal. Build a network graph of accounts that first amplify a meme, and compute centrality metrics—those high‑central nodes often seed the next big trend. Finally, run a simple regression on historical data to forecast the next spike. It’s not magic, just data.

Sure thing. I’d spin up a lightweight crawler that hits Reddit’s hot and Twitter’s trending APIs every minute, logs upvote/share counts, and feeds them into a rolling window model. The script would flag any meme whose engagement curve fits a logistic rise above a threshold. If the signal hits, I’ll publish a quick alert with the meme’s URL and a confidence score. It’s a real‑time proof of concept that turns raw clicks into a predictive edge.