检测阴谋论话语中的细微差别:通过机器学习和定性内容编码,在信息学和通信科学中推进方法
Michael Robert Haupt1,2, Michelle Chiu3, Joseline Chang4
1Department of Cognitive Science, University of California San Diego, La Jolla, California, United States of America.
PloS one
|December 20, 2023
概括
这项研究引入了一种混合方法,将自然语言处理和定性编码结合起来,以分析在线健康错误信息,特别是Twitter上的5G和COVID-19阴谋. 这些发现揭示了阴谋与纠正话语之间明显的语言模式.
科学领域:
- 信息传染病学和计算社会科学.
- 在线健康错误信息和阴谋论的分析.
背景情况:
- 互联网时代出现了错误信息的增加,需要信息流行病学 (信息流行病学) 的进步.
- 现有的机器学习模型在概括性和检测错误信息中的隐含含义方面扎.
- 大量的在线话语需要复杂的方法来识别不断变化的叙事和主题.
研究的目的:
- 开发和演示一种混合方法来检测在线话语中不断演变的阴谋论叙述和细微主题.
- 在Twitter上描述与5G无线技术和COVID-19相关的阴谋话语,包括纠正话语.
主要方法:
- 一种混合方法,将自然语言处理 (话题建模,情感分析) 与定性内容编码相结合.
- 关于5G和COVID-19阴谋论及其纠正的Twitter (X) 数据的分析.
- 情感分析以确定语言模式和诱导编码以描述叙事.
主要成果:
- 阴谋论话语表现出更多的分析性,斗争性,以过去为导向的语言,社会地位参考和负面情绪.
- 纠正性话语包括认知过程,亲社会关系,健康后果和面向未来的语言.
- 阴谋论者叙述包括全球精英,反疫苗的情绪,医疗当局和错误的技术与疾病的相关性;纠正经常错过了关键的阴谋主题.
结论:
- 混合方法有效地分析复杂的在线话语,利用计算和定性方法的优势.
- 了解阴谋论和纠正话语之间的语言和主题差异对于打击错误信息至关重要.
- 进一步整合计算和定性方法可以提高在线健康错误信息的检测和表征.
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