Journal of Computational Social Science

Abstract In an era of deepening political polarization, corporate political activism has emerged as a critical nexus of consumer-brand interaction. This study investigates the alignment between U.S. corporations’ offline political donations and the ideological composition of their online audiences on the X (formerly Twitter) platform. While existing research often relies on surveys, extensive emp…

Abstract Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical…

We present a novel method for tracking the evolution of political dogwhistles—messages which are only understood by a select in-group, while going unnoticed by others (out-group)—in digital environments. Tracking dogwhistles poses a unique empirical challenge due to their reliance on linguistic ambiguity and intentional concealment. To address this, our method combines computational semantics and…

In stock markets, prices often respond to specific anchors such as past peaks adjust only gradually to supply–demand imbalances, rather than reflecting fundamentals immediately. These phenomena suggest path dependence, where investor decisions are influenced not only by current market conditions but also by the trajectory of prices leading up to the present. One plausible driver of such path depe…

Abstract Understanding human mobility is vital to solving societal challenges, such as epidemic control and urban transportation optimization. Recent advancements in data collection now enable the exploration of dynamic mobility patterns in human flow. However, the vast volume and complexity of mobility data make it difficult to interpret spatiotemporal patterns directly, necessitating effective …

Abstract This study presents a novel compartmental mathematical model for analyzing crime and corruption within socio-political systems, with a focus on Colombia, Guatemala, and Venezuela. Recognizing the mutually reinforcing nature of crime and corruption, the model incorporates key population compartments: vulnerable citizens, law enforcement, exposed individuals, corrupt agents, persistent cri…

Abstract This paper presents ongoing work to implement and evaluate virtual humans whose responses to environmental messaging are shaped by their media diets and social interactions. The project scraped thousands of social media post-comment pairs related to environmental issues, classified them by viewpoint through the large-scale orchestration of multiple instances of large language models, and…

Abstract Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human-, GPT3.5-, and Sonnet 3.7-generated stories. Using Explainable Artificial Intelligence (XAI) we test whether features relative to Mednick’s associative theory of creativity can e…

Abstract Politicians with large media visibility and social media audiences have a significant influence on public discourse. Consequently, their dissemination of misinformation can have profound implications for society. This study investigated the misinformation-sharing behavior of 3277 politicians and associated public engagement by using data from X (formerly Twitter) during 2020–2021. The an…

The online version contains supplementary material available at 10.1007/s42001-025-00449-w.