Crisis Communication Patterns in Social Media during Hurricane Sandy
Crisis Communication Patterns in Social Media during Hurricane Sandy
复制标题
桑迪飓风期间社交媒体的危机沟通模式
DOI：
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发表时间：
2017
影响因子：
1.7
通讯作者：
Manuel Cebrian
中科院分区：
文献类型：
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作者：
A. M. Sadri;Samiul Hasan;S. Ukkusuri;Manuel Cebrian
桑迪飓风是过去几十年来最致命、损失最惨重的飓风之一。许多州经历了严重停电；然而，许多人使用社交媒体进行交流，但获得传统信息来源的机会有限或无法获得。这项研究利用机器学习技术，探索了各种通信模式的演变，并确定了桑迪飓风期间出现的用户担忧。原始数据包括 2012 年 10 月 14 日至 2012 年 11 月 12 日期间来自约 1300 万用户的约 5200 万条推文。主题模型针对来自前 4,029 名最常发布有关桑迪的推文至少 100 次的用户的约 76.3 万条推文运行。确定了大约 250 种基于困惑的明确定义的沟通模式。最频繁和相关用户的对话表明了许多风暴阶段（警告、响应和恢复）特定主题的演变。人们还关注风暴的地点和时间、媒体报道以及政治领导人和名人的活动。还提供了导致用户关注的一种特定模式的每个相关关键字。这些关键词对于类似重大灾害的针对性信息传播和有效危机沟通尤其有意义。这些词中的每一个都有助于有效地进行哈希标记，以便根据需要通过社交媒体到达目标受众。本研究的模式识别方法可用于识别未来危机中的实时用户需求。
Hurricane Sandy was one of the deadliest and costliest of hurricanes of the past few decades. Many states experienced significant power outage; however, many people used social media to communicate while having limited or no access to traditional information sources. Using machine learning techniques, this study explored the evolution of various communication patterns and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ∼52M tweets coming from ∼13M users between October 14, 2012 and November 12, 2012. A topic model was run on ∼763K tweets from the top 4,029 most frequent users who tweeted about Sandy at least 100 times. Some 250 well-defined communication patterns based on perplexity were identified. Conversations of the most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. Also presented is each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information-spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach the target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real-time user needs in future crises.