10 citations · 68 across the 39 of their papers we have counts for
13 papers · 1 filter
Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game
Chensong Huang, Changyu Chen, Chenwei Lin +3
LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate thi…
Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
Xinnong Zhang, Wanting Shan, Hanjia Lyu +2
Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these si…
Assessing Historical Structural Oppression Worldwide via Rule-Guided Prompting of Large Language Models
Sreejato Chatterjee, Linh Tran, Quoc Duy Nguyen +6
Traditional efforts to measure historical structural oppression struggle with cross-national validity due to the unique, locally specified histories of exclusion, colonization, and…
SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
Xinnong Zhang, Jiayu Lin, Xinyi Mou +18
Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recen…
Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation
Zhongyi Qiu, Hanjia Lyu, Wei Xiong +1
Social media enables dynamic user engagement with trending topics, and recent research has explored the potential of large language models (LLMs) for response generation. While som…
Irony in Emojis: A Comparative Study of Human and LLM Interpretation
Yawen Zheng, Hanjia Lyu, Jiebo Luo
Emojis have become a universal language in online communication, often carrying nuanced and context-dependent meanings. Among these, irony poses a significant challenge for Large L…
ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents
Xinnong Zhang, Jiayu Lin, Libo Sun +10
The massive population election simulation aims to model the preferences of specific groups in particular election scenarios. It has garnered significant attention for its potentia…
Semantics Preserving Emoji Recommendation with Large Language Models
Zhongyi Qiu, Kangyi Qiu, Hanjia Lyu +2
Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated…
Representation Bias in Political Sample Simulations with Large Language Models
Weihong Qi, Hanjia Lyu, Jiebo Luo
This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion. Using the GPT-…
SoMeLVLM: A Large Vision Language Model for Social Media Processing
Xinnong Zhang, Haoyu Kuang, Xinyi Mou +6
The growth of social media, characterized by its multimodal nature, has led to the emergence of diverse phenomena and challenges, which calls for an effective approach to uniformly…
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Hanjia Lyu, Song Jiang, Hanqing Zeng +7
Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item. However, directly empl…
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
Jinsheng Pan, Weihong Qi, Zichen Wang +2
There is a broad consensus that news media outlets incorporate ideological biases in their news articles. However, prior studies on measuring the discrepancies among media outlets…
Computational Assessment of Hyperpartisanship in News Titles
Hanjia Lyu, Jinsheng Pan, Zichen Wang +1
We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2,200 manually labeled and 1.8 million machine-labeled…