Book Chapters
Gechun Lin and Christopher Lucas. 2023. “An Introduction to Neural Networks for the Social Sciences.” in The Oxford Handbook of Methodological Pluralism in Political Science. Oxford University Press
Working Papers
Gechun Lin. “Think Like A Human: Using Generative AI to Extract Emphasis Frames.”
Abstract: Framing analysis is at the core of studies in political communication. In a polarized environment, media outlets and elites intentionally frame news and policies in a way to advance their political agenda. Two traditional methods: one based on pre-defined linguistic elements and another using inductive topic modeling, both of which have limitations in terms of resources, subjectiveness, and accuracy in capturing evolving discourse. To overcome these issues, I introduce a new approach to using generative AI, specifically large language models like GPT, to extract frames through prompting. Leveraging the model’s extensive training on diverse texts to identify policy frames in news about the smoking ban and moral framing in the party manifestos, this approach offers not only efficiency but also semantic richness. This paper demonstrates the potential of AI in information extraction and highly abstract summarization in framing analysis.
Gechun Lin. “Using Cross-Encoders to Measure the Similarity of Short Texts in Political Science.” (R&R at AJPS)
Abstract: In many settings, scholars wish to estimate the similarity of political texts. However, the most commonly used methods in political science struggle to identify when two texts convey the same meaning as they rely too heavily on identifying words that appear in both documents. This limitation is especially salient when the underlying documents are short, an increasingly prevalent form of textual data in modern political research. Building on recent advances in computer science, I introduce to political science cross-encoders for precise estimates of semantic similarity in short texts. Scholars can use either off-the-shelf versions or build a customized model. I illustrate this approach in three examples applied to social messages generated in a telephone game, news headlines about US Supreme Court decisions, and Facebook posts from members of Congress. I show that cross-encoders, which utilize pair-level embeddings, offer superior performance across tasks relative to word-based and sentence-level embedding approaches.
Dahjin Kim, Gechun Lin, and Keith Schnakenberg. “Informative Campaigns, Overpromising, and Policy Bargaining.” (Under Review)
Abstract: What is the relationship between policy positions taken in campaigns and those proposed in bargaining when the final policy outcome depends on other political actors? Why do candidates sometimes advocate policies in their campaigns that are unlikely or impossible to pass given the preferences of other actors in the government? We analyze a model in which candidates make non-binding policy platform announcements and then bargain with a veto player over the final policy if they take office. In the model, a candidate has private information that is related to the policy preferences of a key citizen group and engages in bargaining with a veto player who is responsive to this information. When the citizen’s group sometimes interprets campaign promises naively, elections are more likely to allow information revelation. Furthermore, in this case, politicians overpromise: the politician’s platform is outside of the range of feasible bargaining outcomes.
Dahjin Kim, Gechun Lin, and William G. Nomikos. “American Social Media is Ideologically Polarized about Foreign Policy during the War in Ukraine.”
Abstract: Political polarization has become a ubiquitous feature of American politics, exacerbated by the rise of online news and social media. While existing research has documented how polarization manifests online, relatively few studies have considered whether these divisions extend to discussions of foreign policy. We examine this question by analyzing nearly 2 million tweets about the war in Ukraine posted by Americans during the opening stages of the Russian invasion. We first categorize each tweet according to the user’s ideological leanings estimated by the network of political accounts they follow. Then, we apply a natural language processing model specifically designed for short texts to classify the tweets into clusters that we hand code into substantive topics. We find that the topic distributions of conservative, moderate, and liberal users are substantively and statistically different. We further find that conservatives are more likely to spread some form of misinformation and that liberals are more likely to express support for Ukraine.
Amaan Charaniya, Weiye (Rex) Deng, Dahjin Kim, Gechun Lin, William G. Nomikos, and Ipek Ece Sener. “How Foreign Policy Crisis Shapes Public Opinion on Social Media: An Investigation of Twitter Discourse during the U.S. Withdrawal from Afghanistan.” (Winner of the Best Paper in Foreign Policy Section at APSA 2023)
Abstract: An emerging consensus has in recent years documented the fundamental interplay between international politics and public opinion. Although it is clear that foreign policy impacts the public opinion toward the political leadership making those decisions, the mechanisms are subject to debate. Additionally, the role of social media in shaping public opinion on foreign policy has been underexplored. This article fills this gap by measuring the public sentiment toward Trump and Biden administrations regarding the U.S. troop withdrawal from Afghanistan. Analyzing 7 million tweets mentioning the critical events during withdrawal from August to September 2021, the study highlights the immediate yet short-lived nature of social media responses to significant foreign policy crisis.