#MeToo, Networked Acknowledgment, and Connective Action: How "Empowerment Through Empathy" Launched a Social Movement. (April 2021)

• Record Type: Journal Article
• Authors: Suk, Jiyoun; Abhishek, Aman; Zhang, Yini; Ahn, So Yun; Correa, Teresa; Garlough, Christine; Shah, Dhavan V.
• Other Names: Boulianne Shelley guest-editor.; Koc-Michalska Karolina guest-editor.; Vedel Thierry guest-editor.
• Is Part Of: Social science computer review. Volume 39:Number 2(2021)
• Page Start: 276
• Page End: 294
• Publication Date: 2021-04
• Subjects: affective public -- connective action -- natural language processing -- retweet network analysis -- time series analysis; Social sciences -- Data processing -- Periodicals; Computers -- Social aspects -- Periodicals; Microcomputers -- Periodicals; Sciences sociales -- Informatique -- Périodiques; Micro-ordinateurs -- Périodiques; 300.285
• DOI: 10.1177/0894439319864882
• Languages: English
• ISSNs: 0894-4393

Abstract

How did efforts that prompted the sharing of personal experiences of sexual violence and harassment around #MeToo coalesce into calls for action across a range of institutions and communities? We argue that sharing experiences of trauma in digital spaces created a network of acknowledgment, which supported and sustained nascent #MeToo activism based on the logic of connective action. This article attempts to (a) understand the temporal dynamics of these different discourses within the #MeToo movement on Twitter, (b) reveal the accounts animating these discourses and the most prominent themes within them, and (c) model the overtime relationship between these discourses and their relationship to major news event and #MeToo revelations. To do so, we analyze a 1% sample of tweets from the 5-month period following the revelations about Harvey Weinstein in early October 2017, employing a range of computational approaches, including part-of-speech tagging, dependency analysis, hashtags extraction, and retweet network analysis—to identify key discourses, actors, and themes. We then conduct time series analysis to identify the relationship between the two discourses and predict how the ebbs and flows of each discourse are shaped by news events.