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Beyond a consciousness-raising hashtag, #MeToo has become a transnational movement, crossing the borders of many societies. However, outsized attention has been paid to the manifestations of #MeToo in the US and on Twitter when the reach of this movement was not restricted to a single country, language, or platform. Drawing from the concept of hybridization, we seek to understand how the uses of #MeToo are contextualized across cultures, languages, and social media platforms. By establishing a macroscopic computational approach, we examine the global diffusion of #MeToo as a hybrid communicative process across different language groups (English, Spanish, and Korean) and social media platforms (Twitter, Facebook, and Instagram). Through time-series analysis and comparative descriptions of language groups and platforms, we demonstrate how discourse flows, language characteristics, and actors differ across cultural and platform contexts and how public discourse of #MeToo was reappropriated and re-signified in different parts of the world to localize connective action. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]