Mapping #MeToo: A synthesis review of digital feminist research across social media platforms

Abstract

A tweet by Hollywood actress Alyssa Milano using Tarana Burke’s phrase “me too” sparked a global movement. Despite the media attention #MeToo has garnered, little is known about how scholars have studied the movement. Through a synthesis review covering sources from 2006 to 2019, we learned that in this time period only 22 studies examined participation on social media such as Twitter and Facebook. We conclude that more research needs to be conducted, particularly to fill a gap in qualitative studies that directly engage individuals, to learn about their experiences with the movement. While #MeToo is a global movement, the omission of any reference to geography or a lack of geographic diversity suggests a narrow focus on scholarship based in the Global North. There is a need for more cross-cultural analysis to gain a better understanding of the movement as it evolves over time and moves into different spaces.

Savvy and woke: Gender, digital profile, social media competence, and political participation in gender issues among young Filipino netizens

“…The third is follower engagement , which involves participation and promotion of the content and activities of other social media users, also coined as metavoicing (Albu and Etter, 2018 ; George and Leidner, 2019 ). Fourth is expressive engagement , which refers to the social media actions and posts that were instigated by users themselves—constructs commonly explored by recent research on gender-related SMPP (Ciszek, 2017 ; Quan-Haase et al, 2021 ). Taking these dimensions together, this framework for SMPP would be able to cater to both positive and negative aspects of wokeness.…”

“…However, the context of the political participation measured in these studies was not specific to gender-issues. Studies that did examine SMPP related to gender issues did not, however, attempt to quantitatively differentiate females from males, and/or cisheterosexual identities from LGBTQ+ counterparts (Jones and Brewster, 2017 ; Foster et al, 2021 ; Quan-Haase et al, 2021 ; Thompson and Turnbull-Dugarte, 2021 ). Our first research objective attempts to test how SMPP-GI is influenced by gender, which covers sex assigned at birth, sexual orientation, and gender identity.…”

“…A wealth of research has indicated the positive influence of social media use on political participation (Dimitrova and Bystrom, 2013 ; Kim and Khang, 2014 ; Boulianne, 2015 ; Lee and Xenos, 2020 ), especially among the youth (Ida and Saud, 2020 ). In terms of focus and context of SMPP, studies have examined generalized forms of online political participation (Shehzad et al, 2021 ), and specific issues such as election (Dimitrova and Bystrom, 2013 ; Yang and DeHart, 2016 ), racism (Ince et al, 2017 ) and gender inequality (Quan-Haase et al, 2021 ).…”

Disclosing #MeToo in 2022

“…Survivors of sexual assault and harassment shared their experiences on Twitter using the hashtag #MeToo (Nutbeam & Mereish, 2021) to raise awareness of the frequency of sexual assault and harassment and inspired calls for social change . Since the movement's height, there has been considerable scholarly engagement with #MeToo (Quan-Haase et al, 2021), including research on the use of the hashtag for sexual assault disclosures and the 'social reactions' to those disclosures (Bogen et al, 2019;Lindgren, 2019;Schneider & Carpenter, 2020). Social reactions refer to how people respond to a survivor's disclosure of sexual victimization, including positive responses such as emotional support or offering resources and negative responses like victim-blaming (Ullman, 2000).…”

A manifesto for computational sociology: The Canadian perspective

“…Computational methods include a gamut of different techniques including machine learning (e.g., deep learning, statistical learning, reinforcement learning), social network analysis, text and data mining (e.g., sentiment analysis, topic modelling, named‐entity recognition), agent‐based modelling, more flexible regression/estimation models (e.g., regression shrinkage and selection, Bayesian statistics, spatial regression models), advances in survey methods (e.g., survey experiments, optimum design, respondent‐driven sampling), and so on. Some sociologists in Canada have contributed directly to the development of particular methods (Alexander & Alkema, 2021; Andersen, 2008; Bignami‐Van Assche et al., forthcoming; Fosse & Winship, 2019; Fox, 2015; Fox & Andersen, 2006; Fu et al., 2020, 2021; Hayduk, 1996; Li et al., forthcoming; Miles, 2016; Nelson, 2020; Stecklov et al., 2018; Wellman et al., 2003, 2020), but more often sociologists have embraced and adapted methods developed by computer scientists, statisticians, and econometricians (Abul‐Fottouh et al., 2020; Boase, 2016; Das, 2022; Gallupe et al., 2019; Gruzd & Mai, 2020; Gu et al., 2021; Hogan & Berry, 2011; Howe et al., forthcoming; Kudla & Parnaby, 2018; Letarte et al., 2021; Li & Luo, 2020; McLevey, 2022; McMahan & McFarland, 2021; Quan‐Haase et al., 2021; Richardson et al., 2021; Roth et al., forthcoming; Shor & Miltsov, 2020; Shor et al., 2013; Silver & Silva, 2021; Smith, 2020; Sytsma et al., 2021; Veenstra & Vanzella‐Yang, 2022; Yuan et al., 2022).…”