Managing visibility on YouTube through algorithmic gossip

Beauty vloggers’ feminised outputs often position them outside of traditional spheres of technical expertise, however, their strategic management of algorithmic visibility makes them an illuminating source of algorithmic knowledge. I draw from an ethnography of beauty vloggers and industry stakeholders to study the collaborative and directive processes used to formulate and sustain algorithmic expertise – algorithmic gossip. Algorithmic gossip is defined as communally and socially informed theories and strategies pertaining to recommender algorithms, shared and implemented to engender financial consistency and visibility on algorithmically structured social media platforms. Gossip is productive: community communication and talk informs and supports practices such as uploading frequently and producing feminised beauty content to perform more effectively on YouTube. Taking gossip seriously can present a valuable resource for revealing information about how algorithms work and have worked, in addition to revealing how perceptions of algorithms inform content production.

[1]  Theresa M. Senft.  Camgirls: Celebrity and Community in the Age of Social Networks , 2008 .

[2]  S. Pink,et al.  Social Media Ethnography: The Digital Researcher in a Messy Web , 2012 .

[3]  C. Potter.  Queer Hoover: Sex, Lies, and Political History , 2007 .

[4]  Ted Striphas.  Algorithmic culture , 2015 .

[5]  J. Littler.  Meritocracy as Plutocracy: The Marketising of ‘Equality’ Under Neoliberalism , 2013 .

[6]  Alice E. Marwick.  Status Update , 2017 .

[7]  Kyra D. Gaunt.  YouTube, Twerking & You: Context Collapse and the Handheld Co‐Presence of Black Girls and Miley Cyrus , 2015 .

[8]  Brian Harmer,et al.  YouTube: Online Video and Participatory Culture , 2010 .

[9]  David B. Nieborg,et al.  The platformization of cultural production: Theorizing the contingent cultural commodity , 2018, New Media Soc..

[10]  Bernhard Rieder,et al.  Conflicts of interest and incentives to bias: A microeconomic critique of Google’s tangled position on the Web , 2014, New Media Soc..

[11]  Florencia García-Rapp.  ‘Come join and let’s BOND’: authenticity and legitimacy building on YouTube’s beauty community , 2017 .

[12]  Sarah Myers West,et al.  Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms , 2018, New Media Soc..

[13]  Bill Ryan,et al.  Making capital from culture : the corporate form of capitalist cultural production , 1993 .

[14]  Taina Bucher,et al.  The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms , 2017, The Social Power of Algorithms.

[15]  David C. Oh,et al.  Vlogging White Privilege Abroad: Eat Your Kimchi's Eating and Spitting Out of the Korean Other on YouTube , 2017 .

[16]  Ariadna Matamoros-Fernández,et al.  From ranking algorithms to ‘ranking cultures’ , 2018 .

[17]  Virginia E. Eubanks.  Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018 .

[18]  Blake Hallinan,et al.  Recommended for you: The Netflix Prize and the production of algorithmic culture , 2016, New Media Soc..

[19]  Karrie Karahalios,et al.  First I "like" it, then I hide it: Folk Theories of Social Feeds , 2016, CHI.

[20]  D. Fitch,et al.  Review of "Algorithms of oppression: how search engines reinforce racism," by Noble, S. U. (2018). New York, New York: NYU Press. , 2018, CDQR.

[21]  Jeremy P. Birnholtz,et al.  "Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media , 2017, CHI.

[22]  E. Meehan.  Conceptualizing culture as commodity: The problem of television , 1986 .

[23]  Kate Crawford,et al.  Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics , 2016 .

[24]  Malte Ziewitz.  Governing Algorithms , 2016 .

[25]  Angela Mcrobbie.  The Politics of Feminist Research: Between Talk, Text and Action , 1982 .

[26]  Phillipp Bergmann,et al.  Music Genres And Corporate Cultures , 2016 .

[27]  Ali Abington.  Gossip and organizations , 2013 .

[28]  R. Kitchin,et al.  Thinking critically about and researching algorithms , 2014, The Social Power of Algorithms.

[29]  Michele Willson,et al.  Algorithms (and the) everyday , 2017, The Social Power of Algorithms.

[30]  Kelley Cotter,et al.  Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram , 2018, New Media Soc..

[31]  Timothy Havens,et al.  Towards a Structuration Theory of Media Intermediaries , 2014 .

[32]  Karrie Karahalios,et al.  Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms , 2014 .

[33]  Karen C. Adkins.  The real dirt: Gossip and feminist epistemology , 2002 .

[34]  C. Abidin.  Visibility labour: Engaging with Influencers’ fashion brands and #OOTD advertorial campaigns on Instagram , 2016 .

[35]  Tarleton Gillespie,et al.  Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem , 2017, The Social Power of Algorithms.

[36]  Paul Dourish,et al.  Algorithms and their others: Algorithmic culture in context , 2016, Big Data Soc..

[37]  Nick Seaver.  Algorithms as culture: Some tactics for the ethnography of algorithmic systems , 2017, Big Data Soc..

[38]  C. Abidin.  #familygoals: Family Influencers, Calibrated Amateurism, and Justifying Young Digital Labor , 2017 .

[39]  Mark Andrejevic,et al.  Exploiting YouTube: Contradictions of user-generated labor , 2009 .

[40]  Kwame Holmes.  What's the Tea Gossip and the Production of Black Gay Social History , 2015 .

[41]  Elizabeth Fish Hatfield.  (Not) getting paid to do what you love: Gender, social media, and aspirational work , 2018 .

[42]  Derek Johnson,et al.  Making Media Work: Cultures of Management in the Entertainment Industries , 2014 .

[43]  P. Vonderau.  The Spotify Effect: Digital Distribution and Financial Growth , 2019 .

[44]  Taina Bucher,et al.  Neither Black Nor Box: Ways of Knowing Algorithms , 2016 .

[45]  Hector Postigo,et al.  The socio-technical architecture of digital labor: Converting play into YouTube money , 2016, New Media Soc..

[46]  Sophie Bishop,et al.  Anxiety, panic and self-optimization , 2018 .

[47]  B. Duffy.  Not) Getting Paid to Do What You Love , 2017 .

[48]  Anamik Saha.  Race and the Cultural Industries , 2018 .

[49]  Zizi Papacharissi.  A Networked Self and Platforms, Stories, Connections , 2018 .