社交媒体情感注释指南 (SMEmo):发展和初始有效性
Susannah B F Paletz1, Ewa M Golonka2, Nick B Pandža2,3
1College of Information Studies, University of Maryland, College Park, MD, USA. paletz@umd.edu.
Behavior research methods
|September 11, 2023
概括
这项研究引入了一种用于测量社交媒体情绪的新方法,增强了我们对在线行为和信息共享的理解. 经过验证的方案准确地量化了不同语言和平台的情绪表达.
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:
- 准确的情绪测量对于了解社交媒体对信息共享的影响至关重要.
- 现有的情绪量化方案缺乏维度,多媒体和背景深度.
- 最近的情感理论需要对社交媒体情感分析进行更新的方法.
研究的目的:
- 开发和验证用于量化社交媒体情绪的标准化注释方案.
- 评估方案在不同语言和媒体类型的可靠性和有效性.
- 提高在数字通信中的情绪测量的维度,多媒体和上下文范围.
主要方法:
- 人类注释者将社交媒体帖子和他们自己的反应评分在情感尺度 (0-100).
- 进行了两项研究:第一项研究 (英语,Twitter/YouTube) 和第二项研究 (波兰语/立陶宛语,Facebook).
- 使用已建立的基于文本的情感指标 (LIWC,VADER,EmoLex,NRC-EIL) 测试了收性和歧视性有效性.
主要成果:
- 在这两项研究中,在多种情绪方面都实现了强大的编码器间可靠性 (类内相关性≥.80).
- 该注释方案显示了合和区分的有效性,即使在非英语样本中相关性较低.
- 该计划成功量化了英语,波兰语和立陶宛语多媒体社交媒体帖子中的情绪.
结论:
- 开发的注释方案为测量社交媒体情绪提供了一个强大而全面的方法.
- 这项研究通过结合更大的情感维度,多媒体内容和上下文理解来推进该领域.
- 这些发现支持该计划在计算社会科学和数字通信领域的各种研究应用中的实用性.
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