纵向和规模地检查与风相关的社交媒体主题:基于变压器的方法
Dhiraj Murthy1, Sophia Elisavet Kurz2, Tanvi Anand2
1Moody College of Communication, Department of Sociology, and School of Information, University of Texas at Austin, Austin, Texas, United States of America.
PloS one
|January 24, 2025
概括
社交媒体,比如Twitter,被用来进行灾难沟通,但帖子往往会被错过. 一个新的GPU加速的BERTopic模型可以从17万条推特中识别关键的风主题,而无需人工培训.
科学领域:
- 自然语言处理自然语言处理.
- 灾难信息学 灾难信息学
- 计算社会科学 计算社会科学
背景情况:
- 在美国,在自然灾害期间,社交媒体平台越来越多地用于公共沟通.
- 大量的社交媒体内容给灾难救援人员带来了挑战,以识别关键信息.
- 以前的机器学习方法需要人类注释员,限制了可扩展性.
研究的目的:
- 评估GPU加速BERTopic模型在风期间识别公众沟通主题的有效性.
- 为了确定自动主题建模是否可以在没有人工培训的情况下识别相关的灾难相关内容.
- 为灾难管理人员提供关于危机期间社交媒体话语的见解.
主要方法:
- 利用了7年来从四次美国风中收集的170万条推特.
- 应用了基于变压器的主题建模技术BERTopic的GPU加速版本.
- 将已识别的主题归类为时间构造,专注于灾难救济,用户关注和天气等主题.
主要成果:
- 用GPU加速的BERTopic模型成功地从大量风相关推特数据集中识别出突出的主题.
- 该模型在不需要人类注释者或预训练的情况下有效运行.
- 确定的主要话题包括灾难救援工作,公众关注和天气状况更新.
结论:
- 使用BERTopic的自动主题建模为在自然灾害期间分析社交媒体提供了可扩展的解决方案.
- 灾害管理人员可以利用这种方法来了解风期间的公众情绪和信息需求.
- 已识别的时间结构为在危机期间社交媒体沟通的不断变化的性质提供了有价值的见解.
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