arXiv subjects

Ziwen Zu

Publications and source records attributed to Ziwen Zu.

2 recordsLinked to original sources

arXiv2026

Talking Politics with Artificial Intelligence

Large language models (LLMs), a prominent form of artificial intelligence (AI), are becoming everyday interfaces for political questions, but most exchanges are dyadic rather than audiencefacing. This paper asks whether AI conversation functions as a new arena for political expression or as a conversational intermediary for routine political demand. Using 4.30 million humanAI conversations from three large public datasets, we apply two validated classifiers to user messages, identifying political content, use case, and expressed ideology. Political content appears in 3.9% of conversations, varies sharply by platform publicness and conversation depth, and is mostly practical: users ask for information, draft text, and process documents far more often than they state opinions. A regression-discontinuity-in-time design around the 2024 U.S. presidential result call shows that the call changed the expressive subset: among U.S. users, stance-taking, affective language, and ideological extremity rose; comparable conversations elsewhere did not. AI conversation is less a public square than a conversational political intermediary, absorbing routine demand and becoming expressive when major events make political stakes explicit.

econ.GN↗

arXiv2023

How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on Over 60 Replicated Studies

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships. However, the identifying assumptions required by an IV design are demanding, and it remains challenging for researchers to assess their validity. In this paper, we replicate 67 papers published in three top journals in political science during 2010-2022 and identify several troubling patterns. First, researchers often overestimate the strength of their IVs due to non-i.i.d. errors, such as a clustering structure. Second, the most commonly used t-test for the two-stage-least-squares (2SLS) estimates often severely underestimates uncertainty. Using more robust inferential methods, we find that around 19-30% of the 2SLS estimates in our sample are underpowered. Third, in the majority of the replicated studies, the 2SLS estimates are much larger than the ordinary-least-squares estimates, and their ratio is negatively correlated with the strength of the IVs in studies where the IVs are not experimentally generated, suggesting potential violations of unconfoundedness or the exclusion restriction. To help researchers avoid these pitfalls, we provide a checklist for better practice.

econ.EM↗