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推特的声音:从俄语的推文中推断出可观察到的主观幸福感
Sergey Smetanin1, Mikhail Komarov1
1HSE University, Moscow, Russia.
PeerJ. Computer science
|June 22, 2023
概括
社交网络的帖子可以测量可观测的主观幸福感 (OSWB). 这项研究使用了Twitter数据和一种新的情绪分析模型来计算俄罗斯的OSWB指数,显示了与传统调查的有希望的相关性.
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:
- 社交网络是关键的沟通平台,反映用户的意见和情绪.
- 在线情绪表达与离线行为平行,使社交媒体成为福祉的数据来源.
- 使用社交媒体数据测量主观幸福感 (OSWB) 需要代表性抽样和准确的情绪分析.
研究的目的:
- 为了计算俄语Twitter人口的OSWB指数.
- 为了验证社会技术互动的情感社会数据模型,使用人口统计和后分层技术对代表性数据进行验证.
- 评估微调的RuRoBERTa-Large模型对俄罗斯社交媒体数据的情绪分析的性能.
主要方法:
- 利用情感社会数据模型进行社会技术交互,结合人口统计信息和分层后.
- 在RuSentiTweet数据集上微调了RuRoBERTa-Large模型,用于情绪分析.
- 从俄语Twitter帖子计算了OSWB指数,并将其与基于调查的影响措施进行了比较.
主要成果:
- 在情绪分析中取得了最先进的结果,F1得分为0.7229.
- 计算的OSWB指标显示了与基于调查的净影响 (r = 0.469, 0.5332) 和积极影响 (r = 0.5177, 0.548) 的中等相关性.
- 这些发现表明,社交媒体数据可以作为衡量人口水平福祉的有价值的代理.
结论:
- 社交媒体数据,特别是推特帖子,可以有效地用于测量俄罗斯的可观察的主观幸福感 (OSWB).
- 开发的情绪分析模型对俄罗斯社交媒体文本表现出高度准确性.
- 这种方法提供了一个可扩展和及时的替代传统调查方法的福利评估.
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