STITCHING POLITICS AND IDENTITY ON TIKTOK
Bibliographic record
Abstract
Though a relative newcomer among social media platforms, social video-sharing platform TikTok is one of the largest social media platforms in the world, boasting over one billion monthly active users, which it garnered in just five years (Dellatto, 2021). While much of the early attention to the platform focused on more frivolous elements, such as its dances and challenges, the political weight of TikTok has become ever clearer. In the 2020 US election, Donald Trump’s plan to fill the 19,000-seat BOK Center in Tulsa was stymied by young activists who reserved tickets with no intention of attending, organized largely on TikTok (Bandy & Diakopoulos, 2020). In the years since, political discourse on TikTok has continued to emerge from everyday users and political campaigns alike (see Newman, 2022), even as TikTok itself has become an object of political contention: calls for banning the app in the United States–citing security concerns influenced by xenophobia, given the app’s Chinese ownership–began in the Trump presidency (Allyn, 2020) and have recently culminated in state- and federal-level bans on the app for government-owned devices in the U.S. (Berman, 2023). While some studies have navigated limited data access and the platform’s relative infancy to offer an examination of political TikTok (see Literat & Kligler-Vilenchik, 2019; Medina Serrano et al., 2020; Vijay & Gekker, 2021; Guinaudeau et al., 2022), there remains a significant need for more analysis and theorization of how TikTok can become both a site for political discourse and a feature caught up within political mobilization. This panel seeks to bring together emerging work that deals with political participation on TikTok, in order to share current wisdom and forge future research directions. The presented works specifically focus on the relationship between political participation on TikTok and political identity for three primary reasons. First, as a video-based and thus embodied platform (Raun, 2012), creator identity is more prominent and easily perceptible in the visual and auditory elements of TikTok videos than in the primarily text-based posts on platforms like Twitter and Facebook. Second, TikTok relies more heavily on its recommendation algorithm for content distribution than its competitors traditionally have (Kaye et al., 2022; Cotter et al., 2022; Zeng & Kaye, 2022; Zhang & Liu, 2021), leading to the creation of “refracted publics” (Abidin, 2021) or Gemeinschaft-style communities (Kaye et al., 2022) around users’ common interests, which may include and/or be heavily informed by identity. Third, TikTok has long prioritized and found success with Generation Z and younger users more broadly (Zeng et al., 2021; Vogels et al., 2022; Stahl & Literat, 2022), which has made generational identity extremely salient on the app, while also implicating political identity, as young people tend to hold political beliefs more cognizant and accepting of diverse identities than older generations (Parker et al., 2019). The papers in this panel consider a wide range of identity characteristics of TikTok users and how these identities shape and are shaped by political discourse on TikTok. Paper 1 builds on TikTok’s targeting of Gen Z, considering the identities of age and generation through a content analysis of political remix on TikTok to uncover how younger users use TikTok for political activism as compared to their older counterparts, and finding evidence that TikTok is a powerful site of collective action. Also building from TikTok’s appeal to GenZ, Paper 2 presents a digital ethnographic analysis of the Trad-Wife phenomena on TikTok, offering that TikTok quietly (and thus insidiously) offers space for the cultivation of Christian Nationalist, ‘gentle fascisms’ within GenZ women, often without mention of ‘politics’ at all. Paper 3 offers a computational content analysis of political posts on TikTok with a focus on the interactions between identity and partisanship, and particularly the ways in which creators of marginalized identities on the right act as identity entrepreneurs, offering conservative critiques of their identity groups in ways which find popularity among conservative audiences of hegemonic identities. Finally, Paper 4 looks at differences in how TikTok users respond to male and female politicians’ TikTok videos using a combination of computational and qualitative methods, with exploratory analysis suggesting that male politicians receive more neutral and positive comments than female politicians. By focusing on identity and political discourse on TikTok, we recognize the wide range of political activity occurring on a platform often denigrated as frivolous, and foreground the importance of identity characteristics to the technological and social shaping of these dialogues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".