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Eyewitness Prediction During Crisis via Linguistic Features

机译：通过语言特征在危机期间的目击者预测

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摘要

Social media is one of the first places people share information about serious topics, such as a crisis event. Stakeholders, including the agencies of crisis response, seek to understand this valuable information in order to reach affected people. This paper addresses the problem of locating eyewitnesses during times of crisis. We included published tweets of 26 crises of various types, including earthquakes, floods, train crashes, and others. This paper investigated the impact of linguistic features extracted from tweets on different learning algorithms and included two languages, English and Italian. Better results than the state of the art were achieved; in the cross-event scenario, we achieved F1-scores of 0.88 for English and 0.86 for Italian; in the split-across scenario, we achieved Fl-scores of 0.69 for English and 0.89 for Italian.
机译：社交媒体是人们分享有关严重主题的信息之一，例如危机事件。 利益攸关方，包括危机代理商的反应，寻求了解这个有价值的信息，以达到受影响的人。 本文解决了在危机时期为目击者定位目击者的问题。 我们列出了出版的各种类型危机的推文，包括地震，洪水，火车崩溃等。 本文调查了不同学习算法中推文提取的语言特征的影响，包括两种语言，英语和意大利语。 达到了比现有技术的更好的结果; 在跨活动场景中，我们为英语实现了0.88的F1分数，意大利语0.86; 在跨越方案中，我们为英语达到了0.69的FL-Scores，意大利语为0.89。

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