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20202026
most citedSense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19

46 citations · 192 across the 56 of their papers we have counts for

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Showing cs.CLShow all

18 papers · 1 filter

cs.CL2026

SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm

Xinnong Zhang, Jiayu Lin, Jia Wang +19

Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples th…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs

Shanshan Wang, Fengying Ye, Hanjia Lyu +6

Previous detection studies have shown that LLMs cannot be effectively used as detectors, but these studies have not addressed modern Chinese poetry. Moreover, no relevant research…

cs.CL2025

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…

cs.CL2025

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese

Hanjia Lyu, Jiebo Luo, Jian Kang +1

While the capabilities of Large Language Models (LLMs) have been studied in both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit differential performance…

cs.CL2025★ 1 cited

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 1 cited

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-…

cs.CL2024

Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection

Jinfa Huang, Jinsheng Pan, Zhongwei Wan +2

Recent advances show that two-stream approaches have achieved outstanding performance in hateful meme detection. However, hateful memes constantly evolve as new memes emerge by fus…

cs.CL2024★ 5 cited

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…

cs.CL2023★ 2 cited

Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets

Jinsheng Pan, Zichen Wang, Weihong Qi +2

Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior…

cs.CL2023★ 9 cited

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…

cs.CL2023★ 2 cited

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…

cs.CL2023★ 2 cited

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…