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Improving automated crisis detection via an improved understanding of crisis language: Linguistic categories in social media crises
Journal of Contingencies and Crisis Management ( IF 3.420 ) Pub Date : 2020-09-29 , DOI: 10.1111/1468-5973.12308
Jonathan Borden 1 , Xiaochen Angela Zhang 2 , Jooyun Hwang 3
Affiliation  

By applying the Linguistic Category Model (LCM) in crisis communication, this study explores the potential of verb tracking on social media to examine how linguistic categories can elucidate the intentional and/or unintentional communication of crisis attribution frames. Through a content analysis, linguistic categories used in both media posts reporting three clusters of crisis and public comments on Facebook were examined. Results indicated that linguistic abstraction in both media post and public comments describing the crisis varied based on crisis cluster, suggesting that the level of linguistic abstraction reflected perceived attribution of responsibility through stability, locus and controllability. Language used to describe preventable crisis tend to be more abstract than those used to describe accidental and victim crisis. Findings of this study empirically tested the integration of LCM in crisis communication and implied potential application of LCM in building automated environmental scanning and crisis prediction systems.

中文翻译:

通过更好地理解危机语言来改善自动危机检测:社交媒体危机中的语言类别

通过在危机沟通中应用语言类别模型(LCM),本研究探索了在社交媒体上进行动词追踪的潜力,以研究语言类别如何阐明危机归因框架的有意和无意交流。通过内容分析,检查了报道三类危机和在Facebook上的公众评论的两个媒体帖子中使用的语言类别。结果表明,描述危机的媒体帖子和公众评论中的语言抽象都基于危机群而变化,这表明语言抽象的水平反映了通过稳定性,场所和可控性感知到的责任归属。用于描述可预防的危机的语言比用于描述意外和受害者危机的语言更抽象。
更新日期:2020-09-29
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