“Pose”: Examining moments of “digital” dark sousveillance on TikTok

Abstract

Over the past year, many young creators who use the Chinese-owned social networking platform TikTok have claimed that its underlying algorithm surveils and suppresses the reach of content by Black, brown, fat, queer, and disabled creators. However, despite these algorithmic biases, these marginalized creators have continued to find new and ingenious ways to not only create but also successfully share anti-racist, anti-misogynistic, LGBTQIA+supportive, and body-positive content on the platform. Using this tension, this essay engages visual content analysis and critical technocultural discourse analysis to examine the innovative ways marginalized creators employ TikTok’s various medium and technological affordances to evade algorithmic surveillance and oppression. Building on Simone Browne’s concept of dark sousveillance, I theorize these practices as acts of digital dark sousveillance, defined within the essay as the use of digital tools to enact surveillance subversion, obfuscation, inversion while operating within systems of racializing surveillance.

TikTok as Television

“…In a similar vein, additional hypothesis driven research should determine the degree to which the dynamics of the ForYou page ever deviate from those of the base page and the degree to which discourses that resonate in the creator community of TikTok as being so heavy handed are in fact continuing to be true. In this sense, this study provides important computational support for arguments made by those including Peterson-Salahuddin (2022) particularly through the ways that it disrupts the defense of corporations to rely on a seemingly neutral or unknowable algorithm.…”

“…In the context of our study of TikTok, we are offering a very early examination of the form. Bhandari and Bimo (2020) and Peterson-Salahuddin (2022) have noted that the feeling of the algorithm and the ways that certain kinds of user are privileged weighs heavy on TikTok users, which is a powerful central theme in interview work. Schellewald (2022) argued that the ways of talk about algorithms on TikTok are an important form of sensemaking about the platform, which call for studies of these central mechanisms both as they are and as users experience them.…”

Freedom dreaming to STEM: A conceptual model for Black youth’s racial and STEM identity development through social media

“…Specifically, PYD has been criticized for not adequately addressing the role of race and ethnicity ( Williams and Deutsch, 2016 ) and structural oppression ( Ginwright and James, 2002 ) in shaping access to opportunities for youth of color. Black youth face racism-related threats in multiple contexts across the life course ( Jones et al, 2020 ; Seaton, 2020 ), and these threats manifest on social media in ways that are both interpersonal (e.g., online racial discrimination; Tynes et al, 2020 ) and structural (algorithm bias; Angwin et al, 2017 ; Peterson-Salahuddin, 2022 ). For example, algorithm bias on social media platforms has been shown to suppress the visibility of content from Black users ( Peterson-Salahuddin, 2022 ).…”

“…Black youth face racism-related threats in multiple contexts across the life course ( Jones et al, 2020 ; Seaton, 2020 ), and these threats manifest on social media in ways that are both interpersonal (e.g., online racial discrimination; Tynes et al, 2020 ) and structural (algorithm bias; Angwin et al, 2017 ; Peterson-Salahuddin, 2022 ). For example, algorithm bias on social media platforms has been shown to suppress the visibility of content from Black users ( Peterson-Salahuddin, 2022 ). This threatens Black youths’ ability to engage positively with race-and STEM-related content on social media.…”

“…Substantiating this notion, Evans (2022) used traditional and digital ethnographic methods to explore Black youths’ (ages 14–24) social media use while they participated in a hip-hop based education program in Chicago. Though participants faced multiple offline (e.g., neighborhood disadvantage, school exclusion) and online (e.g., algorithm bias; see Peterson-Salahuddin, 2022 ) risk factors, the author found that Black youth employed multifaceted strategies (e.g., collective posting, intentional hashtag use, joining shared-interest groups) to resist algorithm bias, increase visibility of their music content, and connect with shared-interest peers both online and offline. These social media strategies helped facilitate Black youths’ sense of connection to their racial and cultural identities while promoting their sense of efficacy as aspiring music professionals.…”