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Zhuoyu Shi

Publications and source records attributed to Zhuoyu Shi.

2 recordsLinked to original sources
arXiv2026

Causal Language in Post Titles Shapes Deeper Topological Structures of Online Conversations

Causal reasoning is fundamental to human understanding and information organization. People prefer causal explanations because they offer coherence, predictability, and a sense of control. Conversational structures shape how knowledge and perspectives are shared, validated, and amplified in networked publics. Understanding the structural effects of causal language can reveal pathways to fostering deeper, more meaningful interactions online. In this work, we investigate how causal language influences the topology and temporal evolution of discussion threads in online conversations with a dataset of 17 million posts across 200 subreddits in 2023 on Reddit. Our results show that causal language is consistently associated with deeper, more sustained conversations, with effects emerging early in the lifecycle of a thread, as demonstrated through a counterfactual experiment. Importantly, emotional responses do not differ substantially between causal language and non-causal language, suggesting that structural depth arises from framing itself rather than affective escalation. A lightweight qualitative analysis shows that causal framing titles prompt users to elaborate more with reasoning and contribute personal experiences, supporting deeper multi-turn exchanges. These findings suggest that causal language acts not merely as a stylistic device, but as a cognitively grounded and structurally influential signal that shapes the topological structures of online conversations.

cs.SI↗
arXiv2026

Gender Attribution in Causal Beliefs

For centuries, women have been cast as the source of harm in public narratives, from witch hunts in early modern Europe to contemporary stereotypes about emotional instability. These cultural patterns reflect enduring biases in how people attribute causality and assign blame, often portraying women as agents of disruption and men as figures of rational authority. In this study, we examine how such gendered causal attributions appear in everyday language. Leveraging three complete 24-hour datasets of all English-language posts on Twitter, and using language models, we extract cause-and-effect relationship pairs and identify gendered attribution of causal agents. We then analyze how gender attribution relates to sentiment, the kinds of effects invoked, and the diffusion of posts through the social networks. Our findings reveal that female-attributed causes are more often associated with negative sentiment and emotional or relational outcomes, whereas male-attributed causes are more frequently linked to positive sentiment and abstract, structural effects. Moreover, male-attributed narratives spread more widely across communities. These results suggest that longstanding gender stereotypes continue to appear in how people express and amplify causal narratives in public discourse, in decentralized, high-velocity environments like social media.

cs.SI↗