Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC

Abstract Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo.

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