VictimFinder: Harvesting rescue requests in disaster response from social media with BERT
VictimFinder: Harvesting rescue requests in disaster response from social media with BERT
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DOI：
10.1016/j.compenvurbsys.2022.101824
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发表时间：
2022-05-17
影响因子：
6.8
通讯作者：
Mandal, Debayan
中科院分区：
文献类型：
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作者：
Zhou, Bing;Zou, Lei;Mandal, Debayan
社交媒体平台在救灾和救援行动中发挥着越来越重要的作用。在紧急情况下，用户可以在社交媒体上发布救援请求沿着他们的地址，而志愿者可以搜索这些消息并发送帮助。然而，在救援行动中有效利用社交媒体仍然具有挑战性，因为缺乏自动和快速识别社交媒体上救援请求消息的工具。分析社交媒体数据（如Twitter数据）在很大程度上依赖于自然语言处理（NLP）算法从文本中提取信息。双向转换器模型的引入，如双向编码器表示转换器（BERT）模型，在许多文本分析任务中显著优于以前的NLP模型，为精确理解和分类不同应用的社交媒体数据提供了新的机会。该研究开发并比较了10个用于识别救援请求推文的受害者模型，其中3个基于里程碑NLP算法，7个基于BERT。在2017年飓风哈维期间发布的3191条手动标记的灾害相关推文被用作训练和测试数据集。我们通过分类精度、计算成本和模型稳定性来评估每个模型的性能。实验结果表明，所有基于BERT的模型都显着提高了救援相关推文的分类准确率。识别救援请求推文的最佳模型是一个定制的基于BERT的模型，带有卷积神经网络（CNN）分类器。其F1得分为0.919，比基线模型高出10.6%。所开发的模型可以促进社会媒体在未来灾害事件中的救援行动。
Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformers (BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events.