Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes towards disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of people's behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management.
翻译:社会媒体已成为公布灾害相关信息的重要渠道,为政府和救济机构提供了实时数据,以便更好地进行灾害管理;然而,这一领域的研究没有得到足够的重视,而且收集有用的信息仍具有挑战性;本文件旨在通过采矿和分析社会媒体数据,提高救灾效率,例如公众对灾害应对的态度,分析社会媒体数据,如公众对不同类型灾害的救灾物资的需求;我们注重基于类型、持续时间和损害等属性的不同自然灾害,其中包括总共41 993份推文;本文通过人工分类推文对公众看法进行定性评估,其中含有对定向救济物资的需求、灾害应对的满意度和公众恐惧等信息;利用8个机器学习模型进行定量分析,研究公众对自然灾害的态度;更好地向决策者提供适当的模型,根据计算时间和预测准确性对机器学习模式进行比较;在不同自然灾害期间改变公众意见,以及人们在面对与推特一样的自然灾害时使用社会媒体进行救灾的行为演变,不断演变;本文件的成果通过定量分析,展示对救灾工作的更好认识和论证;本文件中的成果,展示了各机构对救灾工作的更好认识和拟议进行的研究。