You might also be interested in: recommender algorithms and social imaginary, the case of YouTube
Abstract
Recommender systems are a widespread type of online algorithm, which suggests personalised contents to “digital consumers”. By automatically creating links between items – such as Amazon products, TV series on Netflix, music artists on Spotify – recommender systems co-construct today’s social imaginary. They contribute to shape pop cultures’ “webs of meanings” and trace new symbolic connections shared by media publics. Starting from a recent literature about online algorithms’ power and diffusion, this article aims at problematizing the relationship between recommender systems and social imaginary. The case of the recommender algorithm employed by YouTube, the world’s most popular video sharing web platform, will be presented. Here, it will be interpreted as a twofold technology: on the one hand, the algorithm impacts on the users’ digital experiences; on the other hand, it represents a brand new source of real-time data about the trajectories of contemporary cultures and imaginaries.
Keywords
algorithms; social imaginary; YouTube; social media; recommendation systems; consumption;
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Shepherd H. (2011), The Cultural Context of Cognition: What the Implicit Association Test Tells Us About How Culture Works. Sociological Forum, 26(1): 121-143.
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Ujjin S., Bentley P.J. (2001), Building a Lifestyle Recommender System, Tenth International World Wide Web Conference, 1-5 Maggio 2001, Hong Kong.
Vagni T. (2013), La teoria dei media e l ’immaginario. Uno studio a partire da Edgar Morin. IM@GO, 1: 99-113.
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Winokur M. (2003), The Ambiguous Panopticon: Foucault and the Codes of Cyberspace. CTheory, http://journals.uvic.ca/index.php/ctheory/article/view/14563/5410
Aneesh A. (2009), Global Labor: Algocratic Modes of Organization. Sociological Theory, 27(4): 347–370.
Arnoldi J. (2015), Computer Algorithms, Market Manipulation and the Institutionalization of High Frequency Trading. Theory, Culture & Society, doi: 10.1177/0263276414566642 Arvidsson A., Caliandro A., Airoldi M., Barina S. (2015), Crowds and value. Italian Directioners on Twitter. Information, Communication & Society, doi: 10.1080/1369118X.2015.1064462
Arvidsson A., Delfanti A. (2013), Introduzione ai media digitali, Bologna, Il Mulino.
Baluja S., Seth R., Sivakumar D., Jing Y., Yagnik J., Kumar S., Ravichandran D., Aly M. (2008), Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph, Proceedings of the 17th international conference on World Wide Web (WWW '08), New York, ACM, 895-904.
Barile N., Sugiyama S. (2015), The Automation of Taste: A Theoretical Exploration of Mobile ICTs and Social Robots in the Context of Music Consumption. International Journal of Social Robotics, 7(3): 407-416.
Baudrillard J. (1976), La società dei consumi, Bologna, Il Mulino.
Beer D. (2009), Power through the algorithm? Participatory web cultures and the technological unconscious. New Media & Society, 11(6): 985-1002.
Bendersky M., Garcia-Pueyo L., Harmsen J., Josifovski V., Lepikhin D. (2014), Up Next: Retrieval Methods for Large Scale Related Video Suggestion Categories and Subject Descriptors, KDD’14, 24–27 Agosto 2014, New York, USA.
Boccia Artieri G. (2014), La rete dopo L’overload informativo. La realtà dell'algoritmo da macchia cieca a bene comune. Paradoxa, 2: 100-113.
Boni F. (2006), Teorie dei media, Bologna, Il Mulino.
Boyd D., Crawford K. (2012), Critical Questions for Big Data. Information, Communication & Society, 15(5): 662-679.
Caliandro A. (2014), Ethnography in Digital Spaces: Ethnography of Virtual Worlds, Netnography & Digital Ethnography. In R. Denny, P. Sunderland (a cura di), Handbook of Anthropology in Business, Walnut Creek, CA, Left Coast Press.
Celma O. (2010), Music recommendation and discovery: The long tail, long fail, and long play in the digital music space, Heidelberg, Dordrecht, London, New York, Springer. Cheney-Lippold J. (2011), A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control. Theory, Culture & Society, 28(6): 164-181.
Codeluppi V. (2013), Per una critica dell’immaginario pop: da Benjamin a Baudrillard e ritorno. IM@GO, 1: 87-98.
Codeluppi V. (2014), Miti Fatali. TwinTowers, Beaubourg, Disneyland, America, Andy Warhol, Michael Jackson, Guerra cel Golfo, Madonna, Jeans, Grande Fratello, Milano, Franco Angeli. Davidson J., Liebald B., Liu J. (2010), The YouTube video recommendation system, Proceedings of the ACM RecSys, 293-296.
DiMaggio P. (1997), Culture and Cognition. Annual Review of Sociology, 23: 263-287. Geertz C. (1973), The Interpretation of Cultures, New York, Basic Books.
Gerlitz C., Lury C. (2014), Social media and self-evaluating assemblages: on numbers, orderings and values. Distinktion: Scandinavian Journal of Social Theory, 15(2): 174-188. Giglietto F., Rossi L., Bennato D. (2012), The Open Laboratory: Limits and Possibilities of Using Facebook, Twitter, and YouTube as a Research Data Source. Journal of Technology in Human Services, 30(3-4): 145-159.
Gillespie, T. (2014) The relevance of algorithms. In T. Gillespie, P. Boczkowski, K. Foot (a cura di) Media Technologies: Essays on Communication, Materiality, and Society, Cambridge, MA, MIT Press.
Green J., Burgess J. (2009), YouTube: Online video and participatory culture, Cambridge, Polity Press.
Hallinan B., Striphas T. (2014), Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, doi: 10.1177/1461444814538646 Hennig-Thurau T., Marchand A., Marx P. (2012), Can automated group recommender systems help consumers make better choices?. Journal of Marketing, 76: 89–109.
Hochschild A. R. (2012), The outsourced self: Intimate life in market times, New York, Metropolitan Books.
Jurgenson N. (2012), When Atoms Meet Bits: Social Media, the Mobile Web and Augmented Revolution. Future Internet, 4(4): 83–91.
Konstan J. A., Riedl J. (2012), Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction, 22(1-2): 101–123.
Lambiotte R., Ausloos M. (2005), Uncovering collective listening habits and music genres in bipartite networks. Physical Review E72, 72(6): 066107.
Lash S. (2007), Power after Hegemony: Cultural Studies in Mutation?. Theory, Culture & Society, 24(3): 55–78.
Latour B., Jensen P., Venturini T., Grauwin S., Boullier D. (2012), "The whole is always smaller than its parts”: a digital test of Gabriel Tardes’ monads. The British Journal of Sociology, 63(4): 590–615.
Maffesoli M. (1988), Il tempo delle tribù. Il declino dell’individuo, Roma, Armando Editore. Mahnke M., Uprichard E. (2014), Algorithming the Algorithm. In R. König, M. Rasch (a cura di), Society of the Query Reader: Reflections on Web Search, Amsterdam, Institute of Network Cultures.
Marres N., Weltevrede E. (2013), Scraping the Social? Issues in live social research. Journal of Cultural Economy, 6(3): 313-335.
Marzo P.L., Meo L. (2013), Cartografie Dell’immaginario. IM@GO, 1: 4-17.
McCombs M., Shaw D. (1972), The agenda-setting function of mass media. Public Opinion Quarterly, 36(2): 176-187.
Mackenzie A. (2015), The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5): 429-445.
McKelvey F., Tiessen M., Simcoe L. (2015), A consensual hallucination no more? The Internet as simulation machine. European Journal of Cultural Studies, 18(4-5): 577-594.
Morris J. W. (2015), Curation by code: Infomediaries and the data mining of taste. European Journal of Cultural Studies, 18(4-5): 446-463.
Noelle-Neumann E. (1974), The spiral of silence a theory of public opinion. Journal of Communication, 24(2): 43-51.
Ricci F., Rokach L., Shapira B., Kantor P.B. (2011), Recommender Systems Handbook, Boston, MA, Springer.
Ritzer G. (2005), Enchanting a disenchanted world: Revolutionizing the means of consumption, Newbury Park, CA, Pine Forge Press.
Ritzer G. (2013), Prosumption: Evolution, revolution, or eternal return of the same?. Journal of Consumer Culture, 14(1): 3-24.
Rogers R. (2013), Digital Methods, Boston, MA, MIT Press.
Shepherd H. (2011), The Cultural Context of Cognition: What the Implicit Association Test Tells Us About How Culture Works. Sociological Forum, 26(1): 121-143.
Striphas T. (2015), Algorithmic culture. European Journal of Cultural Studies, 18(4-5): 395- 412.
Surowiecki J. (2007), La saggezza della folla, Fusi orari.
Ujjin S., Bentley P.J. (2001), Building a Lifestyle Recommender System, Tenth International World Wide Web Conference, 1-5 Maggio 2001, Hong Kong.
Vagni T. (2013), La teoria dei media e l ’immaginario. Uno studio a partire da Edgar Morin. IM@GO, 1: 99-113.
Van Dijck J. (2009), Users like you? Theorizing agency in user-generated content. Media, Culture & Society, 31(1): 41-58.
Winokur M. (2003), The Ambiguous Panopticon: Foucault and the Codes of Cyberspace. CTheory, http://journals.uvic.ca/index.php/ctheory/article/view/14563/5410
DOI: https://doi.org/10.7413/22818138050
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Im@go. A Journal of the Social Imaginary - Biannual - Edizioni Mimesis