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Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance

Published:18 October 2021Publication History
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Abstract

Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds-The Identity Strainer Theory-to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users' algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.

References

  1. [n.d.]. About TikTok | TikTok. https://www.tiktok.com/about?lang=enGoogle ScholarGoogle Scholar
  2. [n.d.]. Community Guidelines. https://www.tiktok.com/community-guidelines?lang=enGoogle ScholarGoogle Scholar
  3. [n.d.]. How TikTok recommends videos #ForYou - Newsroom | TikTok. https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you/Google ScholarGoogle Scholar
  4. [n.d.]. TikTok ban: The US is 'looking at' banning Chinese social media apps, Pompeo says - CNN. https://www.cnn.com/2020/07/07/tech/us-tiktok-ban/index.htmlGoogle ScholarGoogle Scholar
  5. [n.d.]. TikTok to launch Transparency Center for moderation and data practices - Newsroom | TikTok. https://newsroom.tiktok.com/en-us/tiktok-to-launch-transparency-center-for-moderation-and-data-practicesGoogle ScholarGoogle Scholar
  6. D. Abrams and M.A. Hogg. 2006. Social Identifications: A Social Psychology of Intergroup Relations and Group Processes. Taylor Francis. https://books.google.com/books?id=50OV4gqcFA0CGoogle ScholarGoogle ScholarCross RefCross Ref
  7. Julia Alexander. 2019. LGBTQ YouTubers are suing YouTube over alleged discrimination. https://www.theverge.com/2019/8/14/20805283/lgbtq-youtuber-lawsuit-discrimination-alleged-video-recommendations-demonetizationGoogle ScholarGoogle Scholar
  8. Oscar Alvarado and Annika Waern. 2018. Towards Algorithmic Experience: Initial Efforts for Social Media Contexts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18. ACM Press, Montreal QC, Canada, 1--12. https://doi.org/10.1145/3173574.3173860Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Nazanin Andalibi and Patricia Garcia. 2021. Sense making and Coping After Pregnancy Loss: The Seeking and Disruption of Emotional Validation Online. Proceedings of the ACM Human Computer Interaction Vol. 5 (April 2021), 31. Issue No. CSCW1. https://doi.org/10.1145/3449201Google ScholarGoogle Scholar
  10. Paul Baker and Amanda Potts. 2013. 'Why do white people have thin lips?' Google and the perpetuation of stereotypes via auto-complete search forms.Critical Discourse Studies10, 2 (May 2013), 187--204. https://doi.org/10.1080/17405904.2012.744320 Publisher: Routledge _eprint: https://doi.org/10.1080/17405904.2012.744320.Google ScholarGoogle Scholar
  11. Ruha Benjamin. 2019.Race after technology: abolitionist tools for the new Jim code. Polity, Medford, MA.Google ScholarGoogle Scholar
  12. Sam Biddle, Paulo Victor Ribeiro, and Tatiana Dias. 2020. Invisible Censorship: TikTok Told Moderators to Suppress Posts by "Ugly" People and the Poor to Attract New Users. https://theintercept.com/2020/03/16/tiktok-app-moderators-users-discrimination/ Library Catalog: The Intercept.Google ScholarGoogle Scholar
  13. Elena Botella. 2019. TikTok Admits It Suppressed Videos by Disabled, Queer, and Fat Creators. https://slate.com/technology/2019/12/tiktok-disabled-users-videos-suppressed.html Library Catalog: slate.com.Google ScholarGoogle Scholar
  14. Engin Bozdag. 2013. Bias in algorithmic filtering and personalization. Ethics and Information Technology 15, 3 (Sept. 2013), 209--227. https://doi.org/10.1007/s10676-013--9321--6Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. André Brock. 2009. "Who do you think you are?": Race, Representation, and Cultural Rhetorics in Online Spaces. Poroi 6, 1 (July 2009), 15--35. https://doi.org/10.13008/2151--2957.1013Google ScholarGoogle Scholar
  16. Melissa Brown, Rashawn Ray, Ed Summers, and Neil Fraistat. 2017. #SayHerName: a case study of intersectional social media activism. Ethnic and Racial Studies40, 11 (Sept. 2017), 1831--1846. https://doi.org/10.1080/01419870.2017.1334934Publisher: Routledge _eprint: https://doi.org/10.1080/01419870.2017.1334934.Google ScholarGoogle Scholar
  17. Jennings Bryant and Peter Vorderer. 2013.Psychology of Entertainment. Routledge. Google-Books-ID: AVnhAQAAQBAJ.Google ScholarGoogle Scholar
  18. Peter J. Burke. 1980. The Self: Measurement Requirements from an Interactionist Perspective. Social Psychology Quarterly 43, 1 (March 1980), 18. https://doi.org/10.2307/3033745Google ScholarGoogle ScholarCross RefCross Ref
  19. Peter J. Burke and Jan E. Stets. 2009.Identity theory. Oxford University Press, Oxford ; New York. OCLC: ocn271647128.Google ScholarGoogle Scholar
  20. Matthew Carrasco and Andruid Kerne. 2018. Queer Visibility: Supporting LGBT+ Selective Visibility on Social Media. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, Montreal QC Canada, 1--12.https://doi.org/10.1145/3173574.3173824Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Michelle Caswell, Marika Cifor, and Mario H. Ramirez. 2016. "To Suddenly Discover Yourself Existing": Uncovering the Impact of Community Archives1. The American Archivist 79, 1 (June 2016), 56--81. https://doi.org/10.17723/0360--9081.79.1.56Google ScholarGoogle ScholarCross RefCross Ref
  22. John Cheney-Lippold. 2011. A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control.Theory,Culture & Society28, 6 (Nov. 2011), 164--181. https://doi.org/10.1177/0263276411424420Google ScholarGoogle ScholarCross RefCross Ref
  23. John Cheney-Lippold. 2017. We are data: algorithms and the making of our digital selves. New York University Press, New York. Proc. ACM Hum.-Comput. Interact., Vol. 5, No. CSCW 2, Article 305. Publication date: October 2021. Algorithmic Folk Theories and Identity on TikTok305:27Google ScholarGoogle Scholar
  24. Robin R. Coleman and Emily Chivers Yochim. 2008. Symbolic Annihilation. In The International Encyclopedia of Communication, Wolfgang Donsbach (Ed.). John Wiley & Sons, Ltd, Chichester, UK, wbiecs124. https://doi.org/10.1002/9781405186407.wbiecs124Google ScholarGoogle Scholar
  25. Kimberle Crenshaw. 1991. Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review43, 6 (July 1991), 1241. https://doi.org/10.2307/1229039Google ScholarGoogle Scholar
  26. Michael Ann Devito, Jeremy Birnholtz, Jeffery T. Hancock, Megan French, and Sunny Liu. 2018. How People Form Folk Theories of Social Media Feeds and What it Means for How We Study Self-Presentation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18. ACM Press, Montreal QC, Canada, 1--12. https://doi.org/10.1145/3173574.3173694Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Michael Ann Devito, Darren Gergle, and Jeremy Birnholtz. 2017. "Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA, 3163--3174. https://doi.org/10.1145/3025453.3025659Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Zak Doffman. [n.d.]. Warning-Apple Suddenly Catches TikTok Secretly Spying On Millions Of iPhoneUsers.https://www.forbes.com/sites/zakdoffman/2020/06/26/warning-apple-suddenly-catches-tiktok-secretly-spying-on-millions-of-iphone-users/ Library Catalog: www.forbes.com Section: Innovation.Google ScholarGoogle Scholar
  29. Bryan Dosono and Bryan Semaan. 2020. Decolonizing Tactics as Collective Resilience: Identity Work of AAPI Communities on Reddit. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (May 2020), 1--20.https://doi.org/10.1145/3392881Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I "like" it, then I hide it: Folk Theories of Social Feeds. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, San Jose California USA, 2371--2382. https://doi.org/10.1145/2858036.2858494Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Motahhare Eslami, Sneha R. Krishna Kumaran, Christian Sandvig, and Karrie Karahalios. 2018. Communicating Algorithmic Process in Online Behavioral Advertising. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18. ACM Press, Montreal QC, Canada, 1--13. https://doi.org/10.1145/3173574.3174006Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, KevinHamilton, and Christian Sandvig. 2015. "I always assumed that I wasn't really that close to [her]": Reasoning about Invisible Algorithms in News Feeds. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15. ACM Press, Seoul, Republic of Korea, 153--162. https://doi.org/10.1145/2702123.2702556Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Nancy Ettlinger. 2018. Algorithmic affordances for productive resistance.Big Data & Society5, 1 (Jan. 2018), 2053951718771399. https://doi.org/10.1177/2053951718771399 Publisher: SAGE Publications Ltd.Google ScholarGoogle ScholarCross RefCross Ref
  34. Ryan J. Gallagher, Elizabeth Stowell, Andrea G. Parker, and Brooke Foucault Welles. 2019. Reclaiming Stigmatized Narratives: The Networked Disclosure Landscape of #MeToo. Proceedings of the ACM on Human-Computer Interaction3, CSCW (Nov. 2019), 96:1--96:30. https://doi.org/10.1145/3359198Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Susan A. Gelman and Cristine H. Legare. 2011. Concepts and Folk Theories. Annual Review of Anthropology 40 (2011),379--398. https://www.jstor.org/stable/41287739 Publisher: Annual Reviews.Google ScholarGoogle ScholarCross RefCross Ref
  36. Tarleton Gillespie. 2010. The politics of 'platforms'. New Media & Society12, 3 (May 2010), 347--364. https://doi.org/10.1177/1461444809342738 Publisher: SAGE Publications.Google ScholarGoogle ScholarCross RefCross Ref
  37. Quentin Grossetti, Cédric du Mouza, and Nicolas Travers. 2019. Community-Based Recommendations on Twitter:Avoiding the Filter Bubble. In Web Information Systems Engineering -- WISE 2019, Reynold Cheng, Nikos Mamoulis, Yizhou Sun, and Xin Huang (Eds.). Vol. 11881. Springer International Publishing, Cham, 212--227. https://doi.org/10.1007/978--3-030--34223--4_14 Series Title: Lecture Notes in Computer Science.Google ScholarGoogle Scholar
  38. Mario Haim, Andreas Graefe, and Hans-Bernd Brosius. 2018. Burst of the Filter Bubble?: Effects of personalization on the diversity of Google News.Digital Journalism 6, 3 (March 2018), 330--343. https://doi.org/10.1080/21670811.2017.1338145Google ScholarGoogle ScholarCross RefCross Ref
  39. Oliver Haimson. 2018. Social Media as Social Transition Machinery.Proceedings of the ACM on Human-Computer Interaction 2, CSCW (Nov. 2018), 63:1--63:21. https://doi.org/10.1145/3274332Google ScholarGoogle Scholar
  40. Monique M. Hennink and Bonnie N. Kaiser. 2020. Saturation in Qualitative Research. In SAGE Research Methods Foundations. SAGE Publications Ltd, 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom.https://doi.org/10.4135/9781526421036822322Google ScholarGoogle Scholar
  41. M.A. Hogg. 2006. Social Identity Theory. In Contemporary Social Psychological Theories, Peter J. Burke (Ed.). Stanford University Press, Palo Alto, CA, 111--136. https://books.google.com/books?id=8Jzkgbq2vYwCGoogle ScholarGoogle Scholar
  42. Nina Huntemann. 2015. No More Excuses: Using Twitter to Challenge The Symbolic Annihilation of Women in Games.Feminist Media Studies 15, 1 (Jan. 2015), 164--167. https://doi.org/10.1080/14680777.2015.987432Google ScholarGoogle Scholar
  43. Instagram. 2020. Introducing Instagram Reels.https://about.instagram.com/blog/announcements/introducing-instagram-reels-announcementGoogle ScholarGoogle Scholar
  44. Sheila Jasanoff. 2004. Afterword. InStates of Knowledge(1 ed.). Routledge, 274--282. Proc. ACM Hum.-Comput. Interact., Vol. 5, No. CSCW2, Article 305. Publication date: October 2021.Google ScholarGoogle Scholar
  45. John C. Turner , Michael A. Hogg, Penelope J. Oakes , Stephen D. Reicher , Margaret S. Wetherell. 1989. Rediscovering the Social Group: A Self-Categorization Theory.Amer. J. Sociology94, 6 (May 1989), 1514--1516. https://doi.org/10.1086/229205Google ScholarGoogle Scholar
  46. Willett Kempton. 1986. Two Theories of Home Heat Control*. Cognitive Science 10, 1 (1986), 75--90. https://doi.org/10.1207/s15516709cog1001_3 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog1001_3.Google ScholarGoogle ScholarCross RefCross Ref
  47. Donald Ervin Knuth. 1997. The art of computer programming(3rd ed ed.). Addison-Wesley, Reading, Mass.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Hanlin Li, Disha Bora, Sagar Salvi, and Erin Brady. 2018. Slacktivists or Activists?: Identity Work in the Virtual Disability March. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, Montreal QC Canada, 1--13. https://doi.org/10.1145/3173574.3173799Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Fannie Liu, Denae Ford, Chris Parnin, and Laura Dabbish. 2017. Selfies as Social Movements: Influences on Participation and Perceived Impact on Stereotypes. Proceedings of the ACM on Human-Computer Interaction1, CSCW (Dec. 2017),72:1--72:21. https://doi.org/10.1145/3134707Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Sorcha Avalon Mackenzie and David Nichols. 2020. Finding 'Places to Be Bad' in Social Media: The Case of TikTok. In Urban Australia and Post-Punk: Exploring Dogs in Space, David Nichols and Sophie Perillo (Eds.). Springer, Singapore, 285--298. https://doi.org/10.1007/978--981--32--9702--9_22Google ScholarGoogle Scholar
  51. Alice E. Marwick and danah boyd. 2011. I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society 13, 1 (Feb. 2011), 114--133. https://doi.org/10.1177/1461444810365313Google ScholarGoogle Scholar
  52. George J. McCall and J. L. Simmons. 1966. Identities and Interactions. Free Press, New York, NY, US. Pages: ix, 278.Google ScholarGoogle Scholar
  53. Sarah McRoberts, Elizabeth Bonsignore, Tamara Peyton, and Svetlana Yarosh. 2016. Do It for the Viewers!: Audience Engagement Behaviors of Young YouTubers. In Proceedings of the The 15th International Conference on Interaction Design and Children - IDC '16. ACM Press, Manchester, United Kingdom, 334--343. https://doi.org/10.1145/2930674.2930676Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Sarah McRoberts, Haiwei Ma, Andrew Hall, and Svetlana Yarosh. 2017. Share First, Save Later: Performance of Selfthrough Snapchat Stories. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA, 6902--6911. https://doi.org/10.1145/3025453.3025771Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Debra Merskin. 1998. Sending up Signals: A Survey of Native American1 Media Use and Representation in the Mass Media. Howard Journal of Communications 9, 4 (Oct. 1998), 333--345. https://doi.org/10.1080/106461798246943Google ScholarGoogle ScholarCross RefCross Ref
  56. Sadia Mir and Christina Paschyn. 2018. Qatar's Hidden Women: Symbolic Annihilation and Documentary Media Practice. Visual Communication Quarterly25, 2 (April 2018), 93--105. https://doi.org/10.1080/15551393.2018.1456932Google ScholarGoogle Scholar
  57. Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, New York.Google ScholarGoogle Scholar
  58. Ihudiya Finda Ogbonnaya-Ogburu, Angela D.R. Smith, Alexandra To, and Kentaro Toyama. 2020. Critical Race Theory for HCI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA,1--16. https://doi.org/10.1145/3313831.3376392Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Bahiyah Omar and Wang Dequan. 2020. Watch, Share or Create: The Influence of Personality Traits and User Motivation on TikTok Mobile Video Usage.International Journal of Interactive Mobile Technologies14, 04 (March 2020), 121--137.https://doi.org/10.3991/ijim.v14i04.12429 Publisher: Universität Kassel.Google ScholarGoogle Scholar
  60. Jahna Otterbacher, Jo Bates, and Paul Clough. 2017. Competent Men and Warm Women: Gender Stereotypes and Backlash in Image Search Results. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA, 6620--6631. https://doi.org/10.1145/3025453.3025727Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Daphna Oyserman, Kristen Elmore, and George Smith. 2012. Self, self-concept, and identity. In Handbook of self and identity, 2nd ed. The Guilford Press, New York, NY, US, 69--104.Google ScholarGoogle Scholar
  62. Eli Pariser. 2012.The filter bubble: how the new personalized web is changing what we read and how we think. OCLC:1120489057.Google ScholarGoogle Scholar
  63. Sarah Perez. 2020. TikTok to open a 'Transparency Center' where outside experts can examine its moderation practices-- Tech Crunch. http://search.proquest.com/docview/2376021221?pq-origsite=summon& Place: New York, United States, New York Publisher: AOL Inc.Google ScholarGoogle Scholar
  64. Emilee Rader and Rebecca Gray. 2015. Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15. ACM Press,Seoul, Republic of Korea, 173--182. https://doi.org/10.1145/2702123.2702174Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Rashawn Ray, Melissa Brown, Neil Fraistat, and Edward Summers. 2017. Ferguson and the death of Michael Brown on Twitter: #BlackLivesMatter, #TCOT, and the evolution of collective identities. Ethnic and Racial Stud-ies40, 11 (Sept. 2017), 1797--1813. https://doi.org/10.1080/01419870.2017.1335422 Publisher: Routledge _eprint:https://doi.org/10.1080/01419870.2017.1335422.Google ScholarGoogle Scholar
  66. Reuters. 2020. TikTok: China's ByteDance agrees to divest US operations after Trump threat. http://www.theguardian.com/technology/2020/aug/01/tiktok-ban-china-bytedance-divest-microsoft Library Catalog: www.theguardian.com Section: Technology.Proc. ACM Hum.-Comput. Interact., Vol. 5, No. CSCW2, Article 305. Publication date: October 2021. Algorithmic Folk Theories and Identity on TikTok305:29Google ScholarGoogle Scholar
  67. Adi Robertson. 2019. TikTok prevented disabled users' videos from showing up in feeds. https://www.theverge.com/2019/12/2/20991843/tiktok-bytedance-platform-disabled-autism-lgbt-fat-user-algorithm-reach-limit Library Catalog:www.theverge.com.Google ScholarGoogle Scholar
  68. Aja Romano. 2019. A group of YouTubers is claiming the site systematically demonetizes queer content. https://www.vox.com/culture/2019/10/10/20893258/youtube-lgbtq-censorship-demonetization-nerd-city-algorithm-reportGoogle ScholarGoogle Scholar
  69. Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cédric Langbort. 2014. Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms.Google ScholarGoogle Scholar
  70. Michael Schuman. [n.d.]. Why America Is Afraid of TikTok - The Atlantic. https://www.theatlantic.com/international/archive/2020/07/tiktok-ban-china-america/614725/'s=09Google ScholarGoogle Scholar
  71. Donghee Shin, Bu Zhong, and Frank A. Biocca. 2020. Beyond user experience: What constitutes algorithmic experiences?International Journal of Information Management 52 (June 2020), 102061. https://doi.org/10.1016/j.ijinfomgt.2019.102061Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Ellen Simpson and Bryan Semaan. Forthcoming. For You, or For "You"?: Everyday LGBTQ+ Encounters with TikTok. Proceedings of the ACM on Human-Computer Interaction CSCW (Forthcoming), 1--33.Google ScholarGoogle Scholar
  73. Anselm L. Strauss and Juliet M. Corbin. 1998.Basics of qualitative research: techniques and procedures for developing grounded theory(2nd ed ed.). Sage Publications, Thousand Oaks.Google ScholarGoogle Scholar
  74. Sheldon Stryker. 1980.Symbolic interactionism: a social structural version. Benjamin/Cummings Pub. Co, Menlo Park, Calif.Google ScholarGoogle Scholar
  75. Benjamin Toff and Rasmus Kleis Nielsen. 2018. "I Just Google It": Folk Theories of Distributed Discovery.Journal of Communication 68, 3 (June 2018), 636--657. https://doi.org/10.1093/joc/jqy009Google ScholarGoogle ScholarCross RefCross Ref
  76. Gaye Tuchman. 2000. The Symbolic Annihilation of Women by the Mass Media. In Culture and Politics, Lane Crothers and Charles Lockhart (Eds.). Palgrave Macmillan US, New York, 150--174. https://doi.org/10.1007/978--1--349--62397--6_9Google ScholarGoogle Scholar
  77. Julia Velkova and Anne Kaun. 2019. Algorithmic resistance: media practices and the politics of repair. Information, Communication & Society(Aug. 2019), 1--18. https://doi.org/10.1080/1369118X.2019.1657162Google ScholarGoogle Scholar
  78. Paul Venzo and Kristy Hess. 2013. "Honk Against Homophobia": Rethinking Relations Between Media and Sexual Minorities. Journal of Homosexuality 60, 11 (Nov. 2013), 1539--1556. https://doi.org/10.1080/00918369.2013.824318Google ScholarGoogle ScholarCross RefCross Ref
  79. Ashley Marie Walker and Michael A. DeVito. 2020. "'More gay' fits in better": Intracommunity Power Dynamics and Harms in Online LGBTQ+ Spaces. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1--15. https://doi.org/10.1145/3313831.3376497Google ScholarGoogle Scholar
  80. Shuaishuai Wang. 2020. Calculating dating goals: data gaming and algorithmic sociality on Blued, a Chinese gay dating app. Information, Communication & Society 23, 2 (Jan. 2020), 181--197. https://doi.org/10.1080/1369118X.2018.1490796Publisher: Routledge _eprint: https://doi.org/10.1080/1369118X.2018.1490796.Google ScholarGoogle ScholarCross RefCross Ref
  81. Yunwen Wang. 2020. Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok (DouYin). Computers in Human Behavior110 (Sept. 2020), 106373. https://doi.org/10.1016/j.chb.2020.106373Google ScholarGoogle Scholar
  82. Michele Willson. 2017. Algorithms (and the) everyday. Information, Communication & Society20, 1 (Jan. 2017), 137--150.https://doi.org/10.1080/1369118X.2016.1200645Google ScholarGoogle Scholar
  83. Svetlana Yarosh, Elizabeth Bonsignore, Sarah McRoberts, and Tamara Peyton. 2016. YouthTube: Youth Video Authorship on YouTube and Vine. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW '16. ACM Press, San Francisco, California, USA, 1421--1435. https://doi.org/10.1145/2818048.2819961Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Lei Zhang, Feng Wang, and Jiangchuan Liu. [n.d.]. Understand Instant Video Clip Sharing on Mobile Platforms:Twitter's Vine as a Case Study. ([n. d.]), 6.Google ScholarGoogle Scholar
  85. Chengyan Zhu, Xiaolin Xu, Wei Zhang, Jianmin Chen, and Richard Evans. 2019. How Health Communication viaTik Tok Makes a Difference: A Content Analysis of Tik Tok Accounts Run by Chinese Provincial Health Committees.International journal of environmental research and public health 17, 1 (2019), 192. https://doi.org/10.3390/ijerph17010192 Place: Switzerland Publisher: MDPI AG.Google ScholarGoogle Scholar
  86. Frederik J. Zuiderveen Borgesius, Damian Trilling, Judith Möller, Balázs Bodó, Claes H. de Vreese, and Natali Helberger. 2016. Should we worry about filter bubbles? Internet Policy Review 5, 1 (March 2016). https://doi.org/10.14763/2016.1.401Google ScholarGoogle ScholarCross RefCross Ref
  87. Diana Zulli and David James Zulli. 2020. Extending the Internet meme: Conceptualizing technological mimesis and imitation publics on the TikTok platform.New Media & Society(Dec. 2020), 1461444820983603. https://doi.org/10.1177/1461444820983603 Publisher: SAGE Publications.Google ScholarGoogle Scholar

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