The Bl@ck and the LGBTQ+ Community on TikTok. A Social Identity Approach to Algospeak Practices

Details

Supervisors

Cougnon, Louise-Amélie

Faculty

Faculté de philosophie, arts et lettres

Degree label

Master [120] en communication multilingue, à finalité spécialisée: langues des affaires

Abstract

Biased algorithmic content moderation and its detrimental effects on minorities are issues that platform users and researchers have observed and are bringing awareness around. Social media users have also taken it upon themselves to devise strategies to bypass algorithmic moderation to keep on discussing banned or controversial topics. On TikTok, the strategy that users have developed is Algospeak, a coded language used to circumvent algorithmic content moderation. Minority groups have been found to be especially vulnerable to algorithmic biases and many marginalised communities now resort to Algospeak to keep on expressing their identity and ideas on TikTok. Focussing on the collective side of Algospeak practices, this thesis takes a social identity approach to Algospeak to research the role of social identification and collective motivations in the Algospeak practices of Black and of LGBTQ+ TikTokers. Life story interviews were conducted with two Black TikTokers and two LGBTQ+ TikTokers and analysed using Braun and Clarke’s (2006) thematic analysis method. The Social Identity Model of Collective Action was chosen as theoretical framework to determine whether the collective components of the model can be applied to Black and LGBTQ+ creators’ Algospeak practices and whether Algospeak can be considered collective action. The analysis showed that the Black and the LGBTQ+ social identities, among other identities, are motivators of Algospeak use and that, in this context, Algospeak use can be considered as collective action, although individual motivations are also present. This study, by adding a collective aspect, broadens the definition of Algospeak and further investigates the reasons behind its use.