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首页> 外文期刊>International journal of web information systems >How do rumors spread during a crisis?: Analysis of rumor expansion and disaffirmation on Twitter after 3.11 in Japan
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How do rumors spread during a crisis?: Analysis of rumor expansion and disaffirmation on Twitter after 3.11 in Japan

机译:危机期间谣言如何传播?:日本3.11之后Twitter上的谣言扩展和不肯定的分析

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Purpose - This aim of this paper is to elucidate rumor propagation on microblogs and to assess a system for collecting rumor information to prevent rumor-spreading. Design/methodology/approach - We present a case study of how rumors spread on Twitter during a recent disaster situation, the Great East Japan earthquake of March 11, 2011, based on comparison to a normal situation. We specifically examine rumor disaffirmation because automatic rumor extraction is difficult. Extracting rumor-disaffirmation is easier than extracting the rumors themselves. We classify tweets in disaster situations, analyze tweets in disaster situations based on users' impressions and compare the spread of rumor tweets in a disaster situation to that in a normal situation. Findings - The analysis results showed the following characteristics of rumors in a disaster situation. The information transmission is 74.9 per cent, representing the greatest number of tweets in our data set. Rumor tweets give users strong behavioral facilitation, make them feel negative and foment disorder. Rumors of a normal situation spread through many hierarchies but the rumors of disaster situations are two or three hierarchies, which means that the rumor spreading style differs in disaster situations and in normal situations. Originality/value - The originality of this paper is to target rumors on Twitter and to analyze rumor characteristics by multiple aspects using not only rumor-tweets but also disaffirmation-tweets as an investigation object.
机译:目的-本文的目的是阐明谣言在微博上的传播,并评估一种收集谣言信息以防止谣言传播的系统。设计/方法/方法-根据与正常情况的比较,我们提供了一个案例研究,说明在最近的灾难情况下,即2011年3月11日的东日本大地震,Twitter上的谣言如何传播。由于难以自动提取谣言,因此我们专门研究了谣言的不肯定之处。提取谣言不定要比提取谣言本身容易。我们对灾难情况下的推文进行分类,根据用户的印象对灾难情况下的推文进行分析,并将灾难情况下的谣言推文与正常情况下的谣言传播进行比较。调查结果-分析结果表明,灾难情况下的谣言具有以下特征。信息传输率为74.9%,代表了我们数据集中最多的推文。谣言推文为用户提供了强大的行为便利,使他们感到消极和愤怒。正常情况的谣言散布在许多层次结构中,而灾难情况的谣言则是两个或三个层次结构,这意味着在灾难情况和正常情况下,谣言传播方式有所不同。原创性/价值-本文的原创性是针对Twitter上的谣言,并不仅使用谣言推文,还使用不肯定性推文作为调查对象,从多个方面分析谣言特征。

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