Great question! It's honestly still imperfect, but here's what helps: 1. Context signals: Emoji ratio (too many often = sarcasm), account age, previous commen

Great question! It's honestly still imperfect, but here's what helps: 1. Context signals: Emoji ratio (too many often = sarcasm), account age, previous comment sentiment in the thread 2. Corroboration: If someone says "to the moon" but options flow shows puts, or there's no volume spike, it gets weighted down 3. Cross-subreddit divergence: If WSB is bullish but r/stocks is bearish on the same ticker, it's often a meme pump 4. Keyword patterns: "bagholding", "loss porn", "wife's boyfriend" = usually ironic/sarcastic context 5. OpenAI sentiment with ticker-specific context: passing the post + surrounding comments helps vs. analyzing in isolation The bigger challenge is that WSB uses sarcasm TO BE sarcastic but also ironically serious sometimes. So I don't try to be 100% accurate - instead, I show confidence scores and let users decide. Would love feedback on better approaches if you've worked on this problem!