在危机事件期间检测和优先考虑需求的实证方法
M Janina Sarol1, Ly Dinh1, Rezvaneh Rezapour1
1University of Illinois at Urbana-Champaign, IL, USA.
概括
这项研究引入了新的方法,以从社交媒体数据中识别危机期间的公共需求. 它有效地提取所需的资源,并指定谁需要它们,帮助应对危机的努力.
科学领域:
- 自然语言处理自然语言处理.
- 危机信息学 危机信息学
- 社交媒体分析 社交媒体分析
背景情况:
- 在危机期间识别公共需求对于资源分配至关重要.
- 像Twitter这样的社交媒体平台为需求评估提供了有价值的,但又杂的数据.
- 挑战包括信息稀缺性和与危机相关的社交媒体数据中不相关的内容.
研究的目的:
- 开发新的方法,在危机期间从社交媒体中检测需求.
- 提取所需资源 (例如口罩,呼吸机) 的列表.
- 识别指定资源需求和受益者 (谁需要什么) 的句子.
主要方法:
- 为需求检测提出了两种不同的自然语言处理方法.
- 将方法应用于COVID-19危机相关推文数据集.
- 对官方列表进行评估的资源提取和对注释的推特进行句子检测.
主要成果:
- 在提取所需资源列表时获得了0.64精度.
- 在检测谁需要什么句子方面获得了0.68F1分数.
- 从杂的社交媒体数据中识别关键信息的有效性.
结论:
- 开发的方法为危机期间的实时需求评估提供了一种可行的方法.
- 自动化社交媒体分析可以显著改善危机资源管理.
- 未来的工作可以为更广泛的危机通信应用改进这些方法.
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