使用深度学习来识别在社交媒体上促进或阻止反疫苗和支持疫苗内容传播的语言特征
Young Anna Argyris1, Nan Zhang2, Bidhan Bashyal3
1Dept of Media and Information, Michigan State University, East Lansing, MI.
概括
反疫苗的社交媒体帖子迅速传播,而支持疫苗的内容则没有. 这项研究确定了推动疫苗内容传播的语言特征,并发现反疫苗推特使用促进者,与支持疫苗的推特不同.
科学领域:
- 计算语言学计算语言学
- 社交媒体分析
- 公共卫生沟通公共卫生沟通
背景情况:
- 社交媒体促进了反疫苗内容的快速传播,增加了犹.
- 在线支持和反对疫苗信息之间存在传播差异.
- 对影响疫苗相关后传播的语言特征的理解有限.
研究的目的:
- 识别促进或抑制疫苗内容传播的语言特征.
- 在反疫苗,支持疫苗和中立推特中比较这些特征的频率.
- 开发一种机器学习模型,用于对疫苗前景的更高阶分类.
主要方法:
- 在Omicron变种激增期间 (2021年11月至12月) 收集了超过1.2亿条推文.
- 采用了两阶段框架,并对微调的BERT分类器进行了微调,用于推特分类.
- 利用语言查询单词计数工具来分析推特特征.
主要成果:
- 反疫苗推文被积极传播 (转发),而支持疫苗的推文则被被动参与 (受欢迎).
- 确定了不同的语言特征集,这些特征促进和抑制了推特的传播.
- 反疫苗推特经常使用促进语言特征;支持疫苗的推特使用抑制特征.
结论:
- 语言特征显著影响与疫苗相关的社交媒体内容的传播.
- 反疫苗内容采用特定的语言策略来增强其影响力.
- 调查结果可以为公共卫生战略提供信息,以有效打击疫苗错误信息.
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