情感内容和在Facebook上分享:一个理论匹配
Susannah B F Paletz1, Michael A Johns2, Egle E Murauskaite3
1College of Information Studies, University of Maryland, College Park, MD, USA.
Science advances
|September 29, 2023
概括
了解社交媒体的病毒性需要检查离散的情绪,而不仅仅是价值或兴奋. 诸如娱乐,愤怒和悲伤之类的特定情绪显著影响帖子共享,为内容策略提供关键见解.
科学领域:
- 社交媒体分析
- 情感的心理学 情感的心理学
- 计算社会科学 计算社会科学
背景情况:
- 情感内容是社交媒体分享帖子的已知预测因素.
- 竞争的情感理论提供了不同的预测,关于情感内容对后病毒性的影响.
研究的目的:
- 测试和比较理论框架,解释情感内容如何影响社交媒体后病毒性.
- 为了确定分立的情绪或更广泛的情绪维度 (价值,兴奋) 是否更好地预测分享行为.
主要方法:
- 分析了来自波兰和立陶宛的4000多个多媒体Facebook帖子.
- 多个团队对帖子进行注释,以识别20多种离散情绪.
- 统计建模用于比较离散情绪的预测力与价值,兴奋,情绪集群和基本情绪模型的预测力,控制混变量.
主要成果:
- 与基于价值,激活/唤醒或情绪集群的模型相比,独立模拟离散情绪显示出更强的解释能力.
- 离散的情感模型与七个基本情感模型相当,但提供了比七个基本情感模型更多的解释能力.
- 特定的分离情绪,包括积极的 (例如,娱乐,可爱/kama muta) 和消极的 (例如,愤怒,悲伤),与增加的帖子共享有关,无论追随者数量,时间,主题或愤怒的反应.
结论:
- 离散的情绪,而不是广泛的价值或兴奋维度,对于理解和预测社交媒体后病毒性至关重要.
- 这些发现提供了对特定情绪和在线共享行为之间的关系的更细致的理解.
- 这项研究为优化社交媒体内容以提高病毒性提供了有价值的见解.
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