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<p>The public’s attribution of responsibility during a crisis is a central process in crisis communication, often explained by the situational crisis communication theory (SCCT). However, SCCT was developed in a pre-social media era, and its applicability in the new ecosystem of algorithm-driven, short-video platforms remains a critical theoretical gap. This study investigated how the core mechanisms of public responsibility attribution are reconfigured in the unique context of China’s leading short-video platform, Douyin. Analyzing 185,148 comments following the tragic Yingcai School fire, our large language model (LLM)-based analysis answered two questions: (1) How are public attributions of responsibility structured in this emotionally charged, algorithmic environment? and (2) How do offline socioeconomic factors shape these digital crisis discourses? Our findings reveal two distinct attribution pathways, namely an anger-accountability track and a sadness-reflection track and demonstrate that critical discourse is systematically linked to regional development. This research provides a crucial empirical validation of SCCT for the short-video era and offers a data-driven guide for context-aware public administration.</p>

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Reconfiguring Responsibility: An Empirical Analysis of Crisis Discourse and Situational Crisis Communication on Douyin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Pu Zhang,
  • Zheng Wei,
  • Feng Kong

摘要

The public’s attribution of responsibility during a crisis is a central process in crisis communication, often explained by the situational crisis communication theory (SCCT). However, SCCT was developed in a pre-social media era, and its applicability in the new ecosystem of algorithm-driven, short-video platforms remains a critical theoretical gap. This study investigated how the core mechanisms of public responsibility attribution are reconfigured in the unique context of China’s leading short-video platform, Douyin. Analyzing 185,148 comments following the tragic Yingcai School fire, our large language model (LLM)-based analysis answered two questions: (1) How are public attributions of responsibility structured in this emotionally charged, algorithmic environment? and (2) How do offline socioeconomic factors shape these digital crisis discourses? Our findings reveal two distinct attribution pathways, namely an anger-accountability track and a sadness-reflection track and demonstrate that critical discourse is systematically linked to regional development. This research provides a crucial empirical validation of SCCT for the short-video era and offers a data-driven guide for context-aware public administration.