김태규온 교수

Abstract Violent hostility between ordinary partisans is undermining American democracy. Social media is blamed for rhetoric threatening violence against political opponents and implicated in offline political violence. Focusing on Twitter, I propose a method to identify such rhetoric and investigate substantive patterns associated with it. Using a data set surrounding the 2020 Presidential Election, I demonstrate that violent tweets closely track contentious politics offline, peaking in the day

Subnational governments in the United States have taken the lead on many aspects of the response to the COVID‐19 pandemic. Variation in government activity across states offers the opportunity to analyze responses in comparable settings. We study a common and informative activity among state officials—state legislators’ attention to the pandemic on Twitter. We find that legislators’ attention to the pandemic strongly correlates with the number of cases in the legislator’s state, the national cou

Abstract Networks among legislators shape politics and policymaking within legislative institutions. In past work on legislative networks, the ties between legislators have been defined on those who serve in the same legislature or chamber. Online information networks, which have been found to play important roles in legislative communication at the national level, are not bounded by individual legislative bodies. We collect original data for over four thousand US state legislators and study pat

Abstract Violent partisan hostility is a growing concern for American democracy, yet the role of partisan elites in driving support for political violence remains underexplored. This is especially problematic given their significant influence over public opinion. How does partisan elites’ rhetoric threatening violence affect support for violence against opposing partisans? Focusing on social media, where violent messages by partisan elites from both sides can reach a wide audience with potential

This article investigates why some former authoritarian ruling parties survive and succeed after democratic transitions, while others decline. We argue that parties formed before the onset of authoritarian rule (“pre-seizure” parties) are better positioned to endure and compete under democracy than those established after authoritarian consolidation (“post-seizure” parties). Pre-seizure parties, having developed strong party brands independent of authoritarian resource advantages, are more resil

Replication codes and data for "Attention to the COVID-19 pandemic on Twitter: Partisan differences among U.S. state legislators"

In this study, we investigate how author affiliation shapes academic discourse, proposing it as an effective proxy for author perspective in understanding what topics are studied, how nations are framed, and whose realities are prioritised.Using Palestine as a case study, we apply BERTopic and Structural Topic Modelling (STM) to 29,536 English-language academic articles collected from the OpenAlex database.We find that domestic authors focus on practical, local issues like healthcare, education,

Large language models are increasingly applied to political discourse, but their ability to detect culturally grounded gender sensitivity remains underexplored. We introduce KOGENT, a benchmark dataset of 1,222 transcripts from the Korean National Assembly, annotated for gender sensitivity across 6,024 utterances. Each utterance is labeled as high or low in gender sensitivity, based on contextual indicators of bias, discrimination, or inclusion, and tagged for the target group. KOGENT spans Kore