arXiv subjects

Jianlong Zhu

Publications and source records attributed to Jianlong Zhu.

3 recordsLinked to original sources

arXiv2026

Divergent Paths to Depolarization: Dialogue Design Shapes the Intergroup Attitudinal Effects of AI-Assisted Political Argumentation

Structured argumentative dialogues where interlocutors deliberate on opposing political ideas are known to promote perspective-taking and reduce political polarization, but finding willing partners is difficult as Americans increasingly shun political discussions. AI dialogue partners offer a scalable framework for such open-mindedness exercises, but how the format of human-AI dialogues shapes their benefits remains unclear. This study seeks to fill the gap with a preregistered two-session online experiment with 527 US participants. As the primary experimental manipulation, participants were assigned to argue either for or against their pre-existing attitude on a contested political issue, engaging either with an AI chatbot or a solitary essay task. The AI conditions further varied in the chatbot's interaction style (adversarial or collaborative) and the presence of an additional financial incentive. The results show that attitude-congruent dialogues more strongly reduced polarization than attitude-incongruent dialogues immediately after the exchange. By contrast, an exploratory analysis suggested a delayed increase in cognitive empathy following attitude-incongruent dialogues, a pattern consistent with the account of sleeper effects. While the conversation style had little influence on the effects of attitude-congruent dialogues, a collaborative discussion tended to make attitude-incongruent dialogues more effective, narrowing the immediate effect gap. Additional financial incentives did not alter outcomes. Given the heterogeneity, the AI conditions were not universally more effective forming favorable intergroup attitudes in pooled comparisons between AI and non-AI conditions. The findings caution against a simplistic view of AI dialogues as a silver bullet for depolarization and highlight dialogue design as a key determinant of effective AI-mediated attitudinal interventions.

cs.CY↗

arXiv2025

Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot

Voting advice applications (VAAs), which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates' political knowledge and engagement. However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced VAAs' click-based interaction with scripted explanations, a conversational chatbot's potential for tailored discussion and deliberate political decision-making remains untapped. Our exploratory mixed-method study investigates how LLM-based chatbots can support voting preparation. We deployed a VAA chatbot to 331 users before Germany's 2024 European Parliament election, gathering insights from surveys, conversation logs, and 10 follow-up interviews. Participants found the VAA chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered VAA chatbots' role as a catalyst for reflection and rationalization. Expanding on participants' desire for transparency, we provide design recommendations for building interactive and trustworthy VAA chatbots.

cs.HC↗

arXiv2022

Embedding-based neural network for investment return prediction

In addition to being familiar with policies, high investment returns also require extensive knowledge of relevant industry knowledge and news. In addition, it is necessary to leverage relevant theories for investment to make decisions, thereby amplifying investment returns. A effective investment return estimate can feedback the future rate of return of investment behavior. In recent years, deep learning are developing rapidly, and investment return prediction based on deep learning has become an emerging research topic. This paper proposes an embedding-based dual branch approach to predict an investment's return. This approach leverages embedding to encode the investment id into a low-dimensional dense vector, thereby mapping high-dimensional data to a low-dimensional manifold, so that highdimensional features can be represented competitively. In addition, the dual branch model realizes the decoupling of features by separately encoding different information in the two branches. In addition, the swish activation function further improves the model performance. Our approach are validated on the Ubiquant Market Prediction dataset. The results demonstrate the superiority of our approach compared to Xgboost, Lightgbm and Catboost.

q-fin.ST↗