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Enregistrement W4392406604 · doi:10.5210/spir.v2023i0.13530

STITCHING POLITICS AND IDENTITY ON TIKTOK

2023· article· en· W4392406604 sur OpenAlexaff
Parker Bach, Adina Gitomer, Melody Devries, Christina Walker, Deen Freelon, Julia Atienza-Barthelemy, Brooke Foucault Welles, Diana Deyoe, Diana Zulli

Notice bibliographique

RevueAoIR Selected Papers of Internet Research · 2023
Typearticle
Langueen
DomaineComputer Science
ThématiqueDigital Media and Philosophy
Établissements canadiensToronto Metropolitan University
Organismes subventionnairesnon disponible
Mots-clésImage stitchingPoliticsIdentity (music)Political scienceComputer scienceArtLawAestheticsArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,002
Études des sciences et des technologies0,0200,036
Communication savante0,0240,024
Science ouverte0,0010,015
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,0130,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,358
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2023
Routes d'admission1
Résumé présentoui

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