Jinyi Ye
@jinyiye.bsky.social
PhD student at USC CS | Multi-agent, Social Simulations, Computational Social Science, Social Networks | Ex MSR, TsinghuaU, MSR Asia
Our study shows that coordination naturally emerges in social simulations with LLM-based agents. When a small group shares a goal and awareness of teammates, they develop human-like coordination strategies without explicit human guidance.
🚨 New preprint 🚨
Can AI agents coordinate influence campaigns without human guidance? And how does coordination arise among AI agents? In our latest research, we simulate LLM-powered AI agents acting like users on an online platform, some benign, some running an influence operation
Can AI agents coordinate influence campaigns without human guidance? And how does coordination arise among AI agents? In our latest research, we simulate LLM-powered AI agents acting like users on an online platform, some benign, some running an influence operation
November 3, 2025 at 9:43 PM
Our study shows that coordination naturally emerges in social simulations with LLM-based agents. When a small group shares a goal and awareness of teammates, they develop human-like coordination strategies without explicit human guidance.
The first large-scale investigation of multimodal AIGC on social media!
📄Pre-print: arxiv.org/abs/2502.11248
📷We made our resources available, including 2M+ labeled images - visit: github.com/angelayejinyi/AIGC-Election-2024
📄Pre-print: arxiv.org/abs/2502.11248
📷We made our resources available, including 2M+ labeled images - visit: github.com/angelayejinyi/AIGC-Election-2024
🚀 First study on multimodal AI-generated content (AIGC) on social media! TLDR: AI-generated images are 10× more prevalent than AI-generated text! Just 3% of text spreaders and 10% of image spreaders drive 80% of AIGC diffusion, with premium & bot accounts playing a key role🤖📢
February 21, 2025 at 5:54 PM
The first large-scale investigation of multimodal AIGC on social media!
📄Pre-print: arxiv.org/abs/2502.11248
📷We made our resources available, including 2M+ labeled images - visit: github.com/angelayejinyi/AIGC-Election-2024
📄Pre-print: arxiv.org/abs/2502.11248
📷We made our resources available, including 2M+ labeled images - visit: github.com/angelayejinyi/AIGC-Election-2024
Are your friends more susceptible to influence than you? Our new work in #ICWSM2025 uncovers The Susceptibility Paradox in online social influence.
w/ @luceriluc.bsky.social , Julie Jiang, @emilioferrara.bsky.social
Explore our findings: arxiv.org/abs/2406.11553
w/ @luceriluc.bsky.social , Julie Jiang, @emilioferrara.bsky.social
Explore our findings: arxiv.org/abs/2406.11553
December 12, 2024 at 6:37 PM
Are your friends more susceptible to influence than you? Our new work in #ICWSM2025 uncovers The Susceptibility Paradox in online social influence.
w/ @luceriluc.bsky.social , Julie Jiang, @emilioferrara.bsky.social
Explore our findings: arxiv.org/abs/2406.11553
w/ @luceriluc.bsky.social , Julie Jiang, @emilioferrara.bsky.social
Explore our findings: arxiv.org/abs/2406.11553
Reposted by Jinyi Ye
A rent paper has been portrayed in the news as proof that Twitter/X new #recsys favors republicans.
In our latest work, collecting millions of datapoints via 120 sock puppets, we should the reality is much more complex: you would be surprised by our findings:
arxiv.org/abs/2411.01852
thread 1/n
In our latest work, collecting millions of datapoints via 120 sock puppets, we should the reality is much more complex: you would be surprised by our findings:
arxiv.org/abs/2411.01852
thread 1/n
Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X Approaching the 2024 U.S. Presidential Election
Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: what political content are users exposed to beyond...
arxiv.org
November 18, 2024 at 5:57 AM
A rent paper has been portrayed in the news as proof that Twitter/X new #recsys favors republicans.
In our latest work, collecting millions of datapoints via 120 sock puppets, we should the reality is much more complex: you would be surprised by our findings:
arxiv.org/abs/2411.01852
thread 1/n
In our latest work, collecting millions of datapoints via 120 sock puppets, we should the reality is much more complex: you would be surprised by our findings:
arxiv.org/abs/2411.01852
thread 1/n