Meysam Alizadeh
@malizadeh.bsky.social
Senior Researcher, University of Zurich, Computational Social Science, Web3 and Social Media | Previously postdoc at Princeton and Harvard
Our second dataset came from a prior survey study (N=1,384) with self-reported usernames and demographics from an international sample, mostly from US and UK.
LLMs achieved higher accuracy than with the synthetic dataset—and showed no gender or political bias.
LLMs achieved higher accuracy than with the synthetic dataset—and showed no gender or political bias.
July 18, 2025 at 5:19 PM
Our second dataset came from a prior survey study (N=1,384) with self-reported usernames and demographics from an international sample, mostly from US and UK.
LLMs achieved higher accuracy than with the synthetic dataset—and showed no gender or political bias.
LLMs achieved higher accuracy than with the synthetic dataset—and showed no gender or political bias.
Our first dataset included 48 synthetic X accounts, generated with GPT4o (tweets, profiles, images).
Even for the 19 accounts that were later suspended, LLMs could still infer age and gender.
We also observed gender and political biases—especially against low-activity accounts.
Even for the 19 accounts that were later suspended, LLMs could still infer age and gender.
We also observed gender and political biases—especially against low-activity accounts.
July 18, 2025 at 5:19 PM
Our first dataset included 48 synthetic X accounts, generated with GPT4o (tweets, profiles, images).
Even for the 19 accounts that were later suspended, LLMs could still infer age and gender.
We also observed gender and political biases—especially against low-activity accounts.
Even for the 19 accounts that were later suspended, LLMs could still infer age and gender.
We also observed gender and political biases—especially against low-activity accounts.
We tested six prompting strategies to infer age, gender, political orientation, and socioeconomic status—using only usernames or X (Twitter) profile links.
Surprisingly, in most cases, usernames outperformed full profile links.
Surprisingly, in most cases, usernames outperformed full profile links.
July 18, 2025 at 5:19 PM
We tested six prompting strategies to infer age, gender, political orientation, and socioeconomic status—using only usernames or X (Twitter) profile links.
Surprisingly, in most cases, usernames outperformed full profile links.
Surprisingly, in most cases, usernames outperformed full profile links.
🚨 New preprint alert:
We show that web-browsing GPT and LLaMA models can infer social media user demographics with reasonable accuracy—using only usernames.
This opens new possibilities for social media research in the post-API era but raises important privacy concerns.
We show that web-browsing GPT and LLaMA models can infer social media user demographics with reasonable accuracy—using only usernames.
This opens new possibilities for social media research in the post-API era but raises important privacy concerns.
July 18, 2025 at 5:19 PM
🚨 New preprint alert:
We show that web-browsing GPT and LLaMA models can infer social media user demographics with reasonable accuracy—using only usernames.
This opens new possibilities for social media research in the post-API era but raises important privacy concerns.
We show that web-browsing GPT and LLaMA models can infer social media user demographics with reasonable accuracy—using only usernames.
This opens new possibilities for social media research in the post-API era but raises important privacy concerns.