Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15289.
Institute of Computing, University of Campinas, Campinas 13083-852, Brazil.
Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2205767119. doi: 10.1073/pnas.2205767119. Epub 2022 Aug 23.
Emotions are a central driving force of activism; they motivate participation in movements and encourage sustained involvement. We use natural language processing techniques to analyze emotions expressed or solicited in tweets about 2020 Black Lives Matter protests. Traditional off-the-shelf emotion analysis tools often fail to generalize to new datasets and are unable to adapt to how social movements can raise new ideas and perspectives in short time spans. Instead, we use a few-shot domain adaptation approach for measuring emotions perceived in this specific domain: tweets about protests in May 2020 following the death of George Floyd. While our analysis identifies high levels of expressed anger and disgust across overall posts, it additionally reveals the prominence of positive emotions (encompassing, e.g., pride, hope, and optimism), which are more prevalent in tweets with explicit pro-BlackLivesMatter hashtags and correlated with on the ground protests. The prevalence of positivity contradicts stereotypical portrayals of protesters as primarily perpetuating anger and outrage. Our work offers data, analyses, and methods to support investigations of online activism and the role of emotions in social movements.
情绪是行动主义的核心驱动力;它们激发了人们参与运动,并鼓励他们持续投入。我们使用自然语言处理技术来分析关于 2020 年黑人的命也是命抗议活动的推文所表达或征求的情绪。传统的现成情感分析工具往往无法推广到新的数据集,也无法适应社会运动如何在短时间内提出新的想法和观点。相反,我们使用少量镜头的领域自适应方法来衡量在这个特定领域感知到的情绪:2020 年 5 月乔治·弗洛伊德去世后,关于抗议的推文。虽然我们的分析在总体帖子中识别出高水平的表达愤怒和厌恶,但它还揭示了积极情绪(包括自豪、希望和乐观)的突出地位,这些情绪在带有明确支持黑人的命也是命标签的推文中更为普遍,并且与实地抗议活动相关。积极性的流行与将抗议者主要描绘为持续愤怒和愤怒的刻板印象相矛盾。我们的工作提供了数据、分析和方法,以支持对在线行动主义和情绪在社会运动中的作用的研究。