Using stylometry to find HN users with alternate accounts

Hacker News

Author here. This site lets you put in a username and get the users with the most similar writing style to that user. It confirmed several users who I suspected were alts and after informally asking around has identified abandoned accounts of people I know from many years ago. I made this site mostly to show how easy this is and how it can erode online privacy. If some guy with a little bit of Python, and $8 to rent a decent dedicated server for a day can make this, imagine what a company with millions of dollars and a couple dozen PhD linguists could do. Here's Paul Graham: https://stylometry.net/user?username=pg Here are some frequent HN commenters: (EDIT: Removed due to privacy concerns)

Visit product View on Hacker NewsNov 26, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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