Mining Help Intent on Twitter During Disasters via Transfer Learning with Sparse Coding
Mining Help Intent on Twitter During Disasters via Transfer Learning with Sparse Coding
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DOI：
10.1007/978-3-319-93372-6_16
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Bahman Pedrood;Hemant Purohit
中科院分区：
文献类型：
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作者：
Bahman Pedrood;Hemant Purohit
在灾害期间，公民在社交媒体上分享各种信息，包括带有寻求或提供帮助的故意行为的信息。及时识别这种帮助意图可以通过帮助响应规划的信息收集和过滤来在操作上有益于灾害管理。先前关于意图识别的研究已经开发了特定于灾难的监督学习方法，使用来自该灾难的标记消息。然而，在新的灾难期间，为了训练监督学习分类器，快速获取大量标记的消息是困难的。在本文中，我们提出了一种新的迁移学习方法，帮助意图识别Twitter在一个新的灾难。该方法使用新的稀疏编码特征表示，有效地传递了过去灾难的标记消息中的意图行为知识。我们的实验使用Twitter的数据从四个灾难事件显示的性能增益高达15%，在F-分数和准确性的基线流行的词袋表示。结果表明，我们的方法的适用性，以协助实时帮助意图识别在未来的灾难。
Citizens share a variety of information on social media during disasters, including messages with the intentional behavior of seeking or offering help. Timely identification of such help intent can operationally benefit disaster management by aiding the information collection and filtering for response planning. Prior research on intent identification has developed supervised learning methods specific to a disaster using labeled messages from that disaster. However, rapidly acquiring a large set of labeled messages is difficult during a new disaster in order to train a supervised learning classifier. In this paper, we propose a novel transfer learning method for help intent identification on Twitter during a new disaster. This method efficiently transfers the knowledge of intent behavior from the labeled messages of the past disasters using novel Sparse Coding feature representation. Our experiments using Twitter data from four disaster events show the performance gain up to 15% in both F-score and accuracy over the baseline of popular Bag-of-Words representation. The results demonstrate the applicability of our method to assist realtime help intent identification in future disasters.