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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.
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An analysis of protesting activity and trauma through mathematical and statistical models
Abstract The effect that different police protest management methods have on protesters’ physical and mental trauma is still not well understood and is a matter of debate. In this paper, we take a two-pronged approach to gain insight into this issue. First, we perform statistical analysis on time series data of protests provided by ACLED and spanning the period of time from January 1, 2020, until March 13, 2021. After observing the data, it becomes apparent that employing kinetic impact projectiles is correlated with an increase in protests in the following days. Moreover, it serves as a more accurate indicator of the subsequent death toll compared to the mere number of protests. This leads to the conclusion that the utilization of less-lethal weapons appears to provoke rather than quell protests, exhibiting an inflammatory effect. Next, we provide a mathematical framework to model modern, but well-established social psychology research on compliance theory and crowd dynamics. Our results show that understanding the heterogeneity of the crowd is key for protests that lead to a reduction of social tension and minimization of physical and mental trauma in protesters.
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- Award ID(s):
- 2042413
- PAR ID:
- 10468391
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Crime Science
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2193-7680
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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