Toward the automatic detection of rescue-request tweets: analyzing the features of data verified by the press
Toward the automatic detection of rescue-request tweets: analyzing the features of data verified by the press
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自动检测救援请求推文：分析媒体验证的数据特征
DOI：
10.1109/ict-dm47966.2019.9032895
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Fujishiro Hiroyuki
中科院分区：
文献类型：
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作者：
Song Chenjie;Fujishiro Hiroyuki
自2011年东日本大地震以来，Twitter已成功地用作自然灾害期间的重要信息渠道。然而，大量的推文越来越多地被附加到“#救援”标签上，用于发送救援请求。这使得很难确定一条推文实际上是否是人为的救援请求，因此需要自动检测。不过，通过这样的手段确认一条推文是否是有效的救援请求并不容易。因此，本研究进行了一项实验，通过分析基于日本广播公司（NHK）提供的数据集的特定推文特征，有效地检测救援请求推文，该数据集验证了与数据集相关的所有人都在2018年日本西部暴雨灾害中获救。我们还将这些推文特征与同一灾难期间收集的另一组推文特征进行了比较，以验证其效果。因此，我们成功地从与其他信息相关的推文中手动识别出救援请求推文，准确率相对较高，为64.7%。
Twitter has successfully been used as a critical information channel during natural disasters since the 2011 Great East Japan Earthquake. However, enormous numbers of tweets are increasingly being attached to the hashtag “#rescue,” which is used to send rescue requests. This makes it difficult to determine whether a tweet is in fact a rescue request by human effort, thus requiring automated detection. However, it is not easy to confirm whether a tweet is a valid rescue request through such means. This study therefore conducted an experiment to effectively detect rescue-request tweets by analyzing specific tweet features based on a dataset provided by the Japan Broadcasting Corporation (NHK), which verified that all people associated with the dataset were rescued during the 2018 heavy-rain disaster in western Japan. We also compared these tweet features to those from another set of tweets collected during the same disaster to verify their effects. We thus succeeded in manually identifying rescue-request tweets from those related to other information with a relatively high accuracy rate of 64.7%.
寻找值得信赖的救济渠道：社会推荐方法
DOI：
10.1109/icedeg.2017.7962553
发表时间：
2017
期刊：
2017 Fourth International Conference on eDemocracy & eGovernment (ICEDEG)
影响因子：
--
作者：
J. Torres
通讯作者：
J. Torres