Stereotypes of the two parties play an important role in political cognition, and a range of recent studies have examined the content and effects of partisan stereotypes. However, little work has studied change in partisan stereotypes over time. We address this question by comparing data on stereotypes of partisans collected before and after the Trump presidency, a time when we might expect individuals' images of the two parties to undergo significant change. Using a structural topic model, we compare responses to open-ended questions asking respondents to list words describing members of the two parties from 2016 and 2021. We find that partisan stereotypes in the 2021 sample are less group- and issue-based and focused more on personal traits. These results suggest that, during the Trump era, members of the mass public came to see the parties in more personalized, character-focused terms, potentially contributing to affective polarization.

One source of the idea that Taiwan independence would be politically desirable is belief in the concept of “Taiwan subjectivity,” which indicates that Taiwan is not an appendage of China but instead an autonomous actor charting its own course – or trying to do so in the face of huge difficulties. The ruling (since 2016) Democratic Progressive Party (DPP) pledges fealty to the goal of ultimately realizing subjectivity but cannot aggressively pursue the agenda because of opposition from the People's Republic of China (PRC), the United States, and some in Taiwan itself. What might that agenda be? Using a Structural Topic Model, we excavate the subjectivity discourse as it developed from 2008 to 2020 in the mainstream DPP-supporting newspaper, the Liberty Times. We find fourteen topics associated with the concept, the most prevalent of which in recent years warn of threats to subjectivity's realization in the political and sociocultural spheres.

What are the major factors contributing to ship accidents, and how do these factors evolve in the long term? This study addresses these two questions by leveraging an unsupervised machine learning method named structural topic modelling to identify the causes of ship accidents. The study analysed 2,341 task errors manually collected from 441 reports issued by four government agencies covering a 45-year time span. The results show that the structure of causes of ship accidents remained essentially the same during this period. This highlights the social-material aspect of navigation technology, indicating that the use of advanced technology may not necessarily lead to safer navigation practices, and the interaction between the technology and human agency must be focused on in the bridge management context. Additionally, the computer-assisted textual data analysis highlights pilot-related factors, which might be rooted in the unsupervised and difficult-to-verify handover procedures between pilots and captains, thereby underlining the importance of appropriate piloting regulations.