Weihong Qi

Jinsheng Pan, Zichen Wang, Weihong Qi et al.

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 research has independently explored framing within news media and by social media users, there remains a notable gap in our comprehension of the disparities in framing political issues between these two distinct groups. To address this gap, we conduct a comprehensive investigation, focusing on the nuanced distinctions both qualitatively and quantitatively in the framing of social media and traditional media outlets concerning a series of American Supreme Court rulings on affirmative action, student loans, and abortion rights. Our findings reveal that, while some overlap in framing exists between social media and traditional media outlets, substantial differences emerge both across various topics and within specific framing categories. Compared to traditional news media, social media platforms tend to present more polarized stances across all framing categories. Further, we observe significant polarization in the news media's treatment (i.e., Left vs. Right leaning media) of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus. The disparities in framing between traditional and social media platforms carry significant implications for the formation of public opinion, policy decision-making, and the broader political landscape.

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-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao Dataset, and China Family Panel Studies to simulate voting behaviors and public opinions. This methodology enables us to examine three types of representation bias: disparities based on the the country's language, demographic groups, and political regime types. The findings reveal that simulation performance is generally better for vote choice than for public opinions, more accurate in English-speaking countries, more effective in bipartisan systems than in multi-partisan systems, and stronger in democratic settings than in authoritarian regimes. These results contribute to enhancing our understanding and developing strategies to mitigate biases in AI applications within the field of computational social science.

Weihong Qi, Fan Huang, Jisun An et al.

This study evaluates the ability of DeepSeek, an open-source large language model (LLM), to simulate public opinions in comparison to LLMs developed by major tech companies. By comparing DeepSeek-R1 and DeepSeek-V3 with Qwen2.5, GPT-4o, and Llama-3.3 and utilizing survey data from the American National Election Studies (ANES) and the Zuobiao dataset of China, we assess these models' capacity to predict public opinions on social issues in both China and the United States, highlighting their comparative capabilities between countries. Our findings indicate that DeepSeek-V3 performs best in simulating U.S. opinions on the abortion issue compared to other topics such as climate change, gun control, immigration, and services for same-sex couples, primarily because it more accurately simulates responses when provided with Democratic or liberal personas. For Chinese samples, DeepSeek-V3 performs best in simulating opinions on foreign aid and individualism but shows limitations in modeling views on capitalism, particularly failing to capture the stances of low-income and non-college-educated individuals. It does not exhibit significant differences from other models in simulating opinions on traditionalism and the free market. Further analysis reveals that all LLMs exhibit the tendency to overgeneralize a single perspective within demographic groups, often defaulting to consistent responses within groups. These findings highlight the need to mitigate cultural and demographic biases in LLM-driven public opinion modeling, calling for approaches such as more inclusive training methodologies.

Weihong Qi

Sentiment analysis, widely critiqued for capturing merely the overall tone of a corpus, falls short in accurately reflecting the latent structures and political stances within texts. This study introduces topic metrics, dummy variables converted from extracted topics, as both an alternative and complement to sentiment metrics in stance classification. By employing three datasets identified by Bestvater and Monroe (2023), this study demonstrates BERTopic's proficiency in extracting coherent topics and the effectiveness of topic metrics in stance classification. The experiment results show that BERTopic improves coherence scores by 17.07% to 54.20% when compared to traditional approaches such as Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), prevalent in earlier political science research. Additionally, our results indicate topic metrics outperform sentiment metrics in stance classification, increasing performance by as much as 18.95%. Our findings suggest topic metrics are especially effective for context-rich texts and corpus where stance and sentiment correlations are weak. The combination of sentiment and topic metrics achieve an optimal performance in most of the scenarios and can further address the limitations of relying solely on sentiment as well as the low coherence score of topic metrics.

Weihong Qi, Kristina Lerman

Large language models (LLMs) frequently encounter inputs that disagree with their prior outputs, through user pushback, retrieved documents, or web search results. While the way they resolve such conflicts -- a process we frame as cognitive dissonance resolution -- has been characterized behaviorally, its connection to internal model uncertainty is not well understood. To study this systematically, we vary persuasion attempts along two dimensions, source authority and evidence quality, across 12 health-science claims of stratified epistemic status. Dissonance can be resolved through persuasion, backfire, or immunity. We introduce Trust Elasticity (TE), an econometrics-inspired measure of how readily a model is persuaded toward conflicting evidence. Across four LLMs, TE varies substantially, while clearly false claims elicit near-zero TE across all models. On two open-weight models, we further find that this variation is associated with two complementary internal uncertainty indicators, Confidence Miscalibration in Qwen and Internal Uncertainty Change in Llama. These results link cross-model behavioral variation to a measurable internal property and point to interventions targeting internal uncertainty as future work.

Hanjia Lyu, Weihong Qi, Zhongyu Wei et al.

Leveraging Large Multimodal Models (LMMs) to simulate human behaviors when processing multimodal information, especially in the context of social media, has garnered immense interest due to its broad potential and far-reaching implications. Emojis, as one of the most unique aspects of digital communication, are pivotal in enriching and often clarifying the emotional and tonal dimensions. Yet, there is a notable gap in understanding how these advanced models, such as GPT-4V, interpret and employ emojis in the nuanced context of online interaction. This study intends to bridge this gap by examining the behavior of GPT-4V in replicating human-like use of emojis. The findings reveal a discernible discrepancy between human and GPT-4V behaviors, likely due to the subjective nature of human interpretation and the limitations of GPT-4V's English-centric training, suggesting cultural biases and inadequate representation of non-English cultures.