用户的个性特征如何预测社交媒体中用户生成内容的情绪趋势:配置分析和机器学习的混合方法
Yongqing Yang1,2, Jianyue Xu2,3, Ling Zhao4
1Shenyang University of Technology, Shenyang, China.
Journal of personality
|December 18, 2024
概括
用户的个性特征会影响社交媒体的情绪. 这项研究表明,人格特征的组合,而不是单个特征,可以预测用户生成的内容情绪倾向.
科学领域:
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
- 自然语言处理自然语言处理.
背景情况:
- 社交媒体上的用户生成内容 (UGC) 表现出不同的情绪趋势.
- 这些倾向可以塑造公众论,有时是不合理的.
- 了解用户个性和内容情绪之间的联系至关重要.
研究的目的:
- 调查用户的个性特征与其UGC的情感倾向之间的关系.
- 探索不同的人格配置如何影响在线情感表达.
主要方法:
- 收集了来自1215名Twitter用户的18686条推文.
- 采用机器学习模型,使用Essays和Sentiment数据集进行训练,以识别人格特征和情绪.
- 使用清晰的定性比较分析 (csQCA) 来分析人格配置和情绪倾向.
主要成果:
- 没有任何一个个性特征被发现是UGC中特定情绪倾向的必要条件.
- 识别了多种不同的人格特征的配置,这些特征同样导致了特定的情绪倾向.
结论:
- 在UGC中观察到的情绪模式是各种个性特征维度的复杂配置的结果.
- 这项研究强调了在理解在线情绪时考虑多维人格互动的重要性.
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