识别和量化社交网络服务帖子中的认可欲望,并分析它们的语言特征
Erina Murata1, Kiichi Tago2, Qun Jin3
1Graduate Student of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan.
Acta psychologica
|March 14, 2026
概括
研究人员开发了一种机器学习框架,以识别和量化社交网络服务 (SNS) 中的认可欲望. 这种方法有助于了解用户行为和内容特征,在分类推文中达到67%的准确率.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 计算语言学 计算语言学
背景情况:
- 在社交网络服务 (SNSs) 中,获得认可的愿望是一个重要的因素,但很难量化.
- 有限的研究客观地检查了读者对帖子中的认可欲望的看法.
研究的目的:
- 引入一个新的框架来识别和量化SNS职位的认可愿望.
- 分析与认可欲望相关的语言特征和后特征.
主要方法:
- 在7078个日本推特帖子上利用机器学习,统计分析和文本挖掘.
- 开发了两个指标:支持愿望的帖子比例和每条推文支持愿望的强度.
- 进行语言特征分析和统计假设测试.
主要成果:
- 机器学习方法在分类推文中实现了大约67%的最大巧合率.
- 拥有高认可欲望的用户发帖频率较低,但每篇文章使用的单词较多.
- 识别了特定的语言特征,表明人们渴望获得认可.
结论:
- 拟议的框架有效量化了SNS职位的认可愿望.
- 调查结果提供了关于内容特征和与寻求批准相关的用户行为的见解.
- 推特的机器学习分类为了解在线社交互动提供了有价值的数据.
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