Hacker News Sentiment: 65% Negative, 27% More Engagement - philippdubach.comSkip to main contentphilippdubach<br>Quantitative finance, AI, and the economics underneath.

Negativity Bias and Engagement on Hacker News<br>This Hacker News sentiment analysis began with a simple observation: posts with negative sentiment average 35.6 points on Hacker News. The overall average is 28 points. That&rsquo;s a 27% performance premium for negativity.

×This finding comes from an empirical study I&rsquo;ve been running on HN attention dynamics, covering decay curves, preferential attachment, survival probability, and early-engagement prediction. The preprint is available on SSRN. I already had a gut feeling. Across 32,000 posts and 340,000 comments, nearly 65% register as negative. This might be a feature of my classifier being miscalibrated toward negativity; yet the pattern holds across six different models.<br>Six-Model Sentiment Comparison: Transformers vs LLMs

×I tested three transformer-based classifiers (DistilBERT, BERT Multi, RoBERTa) and three LLMs (Llama 3.1 8B, Mistral 3.1 24B, Gemma 3 12B). The distributions vary, but the negative skew persists across all of them (inverted scale for 2-6). The results I use in my dashboard are from DistilBERT because it runs efficiently in my Cloudflare-based pipeline.<br>What counts as &ldquo;negative&rdquo; here? Criticism of technology, skepticism toward announcements, complaints about industry practices, frustration with APIs. The usual. It&rsquo;s worth noting that technical critique reads differently than personal attacks; most HN negativity is substantive rather than toxic. But, does negativity cause engagement, or does controversial content attract both negative framing and attention? Probably some of both.<br>HackerBook Dataset: Cross-Validation With 22GB of Hacker News Data<br>Related to this, I also saw this Show HN: 22GB of Hacker News in SQLite, served via WASM shards. Downloaded the HackerBook export and ran a subset of my paper&rsquo;s analytics on it.<br>Caveat: HackerBook is a single static snapshot (no time-series data). Therefore I could not analyze lifecycle analysis, early-velocity prediction, or decay fitting. What can be computed: distributional statistics, inequality metrics, circadian patterns.

×Score Distribution and Power-Law Fit

×Attention Inequality: Lorenz Curve and Gini Coefficient

×Related<br>Social Media Success Prediction: BERT Models for Post TitlesCircadian Posting Patterns

×Score vs Comment Engagement

№ 028·<br>2026-01-07<br>2 min·<br>AI·<br>Updated 2026-03-15<br>Topics:Hacker News sentiment analysis<br>negativity bias social media engagement<br>BERT RoBERTa sentiment comparison<br>attention inequality power law social media<br>Hacker News data analysis engagement

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