AI 中文总结
本研究利用Twitter英文帖子数据集，通过语言模型分析发现，社交媒体中存在性别化因果归因，女性关联负面情感与关系结果，男性关联正面情感与结构结果，且男性叙事传播更广，反映性别刻板印象仍存在于公共话语中。
AI 中文摘要
几个世纪以来，从近代欧洲的猎巫行动到当代关于情绪不稳定的刻板印象，女性一直被塑造为公共叙事中伤害的来源。这些文化模式反映了人们在归因因果关系和分配责任方面长期存在的偏见，常将女性描绘为破坏性行为的主体，而男性则被塑造为理性权威的形象。本研究探讨这种性别化因果归因如何出现在日常语言中，利用三个完整的24小时时间段内Twitter上所有英文帖子的数据集，通过语言模型提取因果关系对并识别因果主体的性别归因，进而分析性别归因与情感、所提及的效应类型及帖子在社交网络中的传播情况的关联。研究结果显示，被归因于女性的原因更多与负面情感及情绪或关系性结果相关，而被归因于男性的原因则更常与正面情感及抽象的结构性结果相关；此外，男性归因的叙事在不同社群中传播得更广泛。这些结果表明，长期存在的性别刻板印象仍会出现在社交媒体这类分散化、高速度的环境中，人们表达和放大因果叙事的过程里。
英文摘要
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.
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