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Cross-Cultural Reactions to Crisis Events via Language and Emoticon Use

机译:通过语言和表情符号使用对危机事件的跨文化反应

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We analyze language and emoticon use on a social media platform to describe large-scale human reaction to a crisis event. We focus on a targeted corpus of 2 million tweets defined by a set of hashtags and posted on the social media platform Twitter. We analyze these data under the framework of Con-strual Level Theory and compute lexical diversity and concreteness values of words across subsets of the data that differ with respect to geographical distance from the event. We find that word count and lexical variation among concrete words (but not average concreteness) decreased with increasing geographical distance. In addition, we investigate the use of non-verbal signals through the use of emoticons and emojis in these subsets. Overall, our findings make significant contributions in quantifying and contrasting cross-cultural communication with respect to large-scale human response to a crisis event, specifically a terrorist attack. The results presented here are novel in that they demonstrate what we learn from large-scale nonverbal as well as verbal communication analyzed in the framework of the Construal Level Theory.
机译:我们分析了社交媒体平台上的语言和表情用途,以描述对危机事件的大规模人类反应。我们专注于由一组具有一组标签定义的200万推文的目标语料库,并在社交媒体平台推特上发布。我们根据Con-Strual Level理论的框架分析了这些数据,并在与从事件的地理距离不同的数据中计算词汇分集和分量的单词的具体化值。我们发现,随着地理距离的增加,具体话语(但不是平均具体)之间的字数和词汇变化降低。此外,我们通过在这些子集中使用表情符号和表情符号来调查非口头信号的使用。总体而言,我们的调查结果对量化和对比跨文化交际对危机事件的大规模人类反应来说,做出了重大贡献,特别是恐怖袭击。这里提出的结果是新颖的,因为他们证明了我们从大规模非语言中学到的内容以及在构建水平理论的框架中分析的口头通信。

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