级联语义分类用于在社交媒体中识别域名
James Danowski1, Ken Riopelle2, Bei Yan3
1Department of Communication, University of Illinois at Chicago, Chicago, IL, United States.
Frontiers in research metrics and analytics
|March 18, 2024
概括
级联语义分成 (CSF) 通过代地删除无关链接来改进社交媒体数据. 这种方法澄清了感兴趣的语义领域,追踪了COVID-19实验室泄漏理论从阴谋论到主流讨论的演变.
科学领域:
- 社交网络分析 社交网络分析
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
背景情况:
- 社交媒体数据分析受到多语法和抽象性的噪音的挑战.
- 现有的方法很难在大型数据集中隔离特定的语义域.
- 跨领域内容经常会掩盖社交媒体文本中的相关信息.
研究的目的:
- 引入和演示级语义分成法 (CSF) 以隔离特定的语义域.
- 系统地从社交媒体数据中删除不相关的跨域链接.
- 随着时间的推移,追踪特定主题的语义领域的演变.
主要方法:
- 在语义网络中,CSF采用代修剪和社区检测.
- 该过程定位语义群体,排除群体间的联系,并完善社区检测.
- 分析利用了2020年2月至2021年3月的公共Facebook帖子 (95K) 关于武汉实验室泄漏理论.
主要成果:
- CSF成功地识别和分离了与实验室泄漏理论相关的语义域.
- 分析显示,随着时间的推移,话语从阴谋转向意外释放.
- 该方法捕捉了语义领域的演变,因为它从社交媒体转向主流媒体.
结论:
- 级联语义分成是有效的澄清和隔离在社交媒体上的语义领域.
- CSF可以追踪在线讨论主题的时间演变.
- 该技术提供了一个强大的方法来分析复杂的信息景观.
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