In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.
翻译:近年来,我们面临一系列自然灾害,造成大量财政、环境和人命损失。自然灾害行为不可预测的性质使得很难有一个全面的形势意识(SA)来支持灾害管理。使用民意调查是分析自然灾害期间公众关注事项的传统方法;然而,这一方法有限、昂贵、耗时。幸运的是,社交媒体的出现为学者提供了分析公众关注事项的替代手段。社交媒体使用户(人民)能够自由表达自己的意见并散布关于当前事件(包括自然灾害)的信息。这一研究强调了社会媒体分析的价值,并提出了一个分析框架:Twitter情景意识(TwiSA ) 。这个框架使用文字采矿方法,包括情绪分析和主题模型,为备灾、应灾和恢复创建一个更好的SA。TwiSA还有效地在2015年南卡罗莱州洪水期间,在大量推特上投放,跟踪人们的负面关切。