This project situates our analysis in discourses on sexual violence and gender justice activism on Twitter, Facebook, Instagram, and Reddit. We propose a three-pronged framework to understand discourses surrounding social media activism initiated by networked counterpublics: personal expression that raises public consciousness and fosters social support, calls to action that demand social changes, and contention that can either invigorate or stymie action. We use supervised machine learning to classify three discourses (networked acknowledgement, calls to action, and feminism contention) and apply time series analysis to model their interrelations.
Results show that networked acknowledgment stimulates both calls to action and feminism contention and that calls to action predicts feminism contention across all platforms. Also, these discourses were more sensitive to various events on Twitter and Facebook, but more ephemeral and cyclical on Instagram and more persistent and mutually reinforcing on Reddit. These findings underscore the opportunities and challenges in social media activism and articulate cross-platform similarities and differences.
With existing research showing the fragilities of social media activism and the stubbornness of partisan beliefs and attitudes, we examine whether partisans politicize social media activism. Politicization is conceptualized as partisans crowding out non-partisan participants, dominating conversations and debates, and weaving selective events into narratives. Our large-scale and multi-layered computational analysis of discourses surrounding the #MeToo movement is based on a comprehensive list of related accusations and a vast corpus of all #MeToo tweets. Through cluster analysis of the retweet network, we identified different groups and found that partisan users accounted for the overwhelming majority of the tweets. In the next step, we will apply supervised machine learning to classify different expressions and use time series modeling to examine the relationship between features of accusations and types of expressions.
This project explores how the discourse surrounding a racially marginalized community becomes politicized via culture wars narratives across platforms. Drawing from the thesis of identity politics and political polarization, we discuss how racial justice discourse is hijacked by partisan leaders and their followers over time and explain how this process plays out through both differentiated and coordinated social media platform use. Situating our discussion in the ‘woke’ discourse as originated within the Black Twitter community, we collect data from 2012 through 2022 on Twitter and YouTube and apply user and content-level computational analyses to reveal that the term woke has been increasingly amplified in culture wars on both platforms over time. In particular, partisan users on Twitter dominated and politicized the discourse while lifestyle and entertainment channels on YouTube were most prevalent. The cross-platform analysis reveals that YouTube was a top domain within external links embedded in tweets, and Twitter was also a top destination within the external links embedded in YouTube video descriptions. Conservatives were especially keen on sharing right-wing content to interconnect platforms, suggesting the success of cross-platform coordination in helping them amplify culture war issues. Both theoretical and political implications are discussed.
Centering on social media’s public- and profit-oriented nature, this project theorizes how social media users are empowered and constrained when participating in platform governance. The empirical analysis focuses on user responses before and after Elon Musk’s official acquisition of Twitter, utilizing cluster analysis and topic modeling to examine the volume and content of related discourses among Twitter user groups. Our results point to user constraint in platform governance. Though a diverse set of users, such as partisans, bots, and cryptocurrency enthusiasts, participated with diverging objectives, partisans dominated the conversations. There was an upsurge in user volume and activity level post-acquisition among liberal users, whose critical voices on platform governance might have bolstered platform business. Potential bots also increased in volume and amplified political topics. Our findings shed light on the challenges of user-driven platform governance, underscoring the complex interplay between platform users, economy, and governance.