期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：35
DOI：10.1073/pnas.2205767119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
In a corpus of 34 million tweets about Black Lives Matter from June 2020, although negative emotions, like anger and disgust, occur commonly, positive emotions, like hope and optimism, are more prevalent in tweets with pro-BlackLivesMatter hashtags and significantly correlated with the presence of on the ground protests. These results contrast “angry Black” stereotypes and portrayals of protesters as perpetuating anger and outrage. This work demonstrates how natural language processing techniques can shed insight into social movements and counter harmful stereotypes, and also, it offers methodology for extracting social meaning from text data.
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.
关键词：enemotion analysisBlackLivesMatterTwitternatural language processing
