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G Model POETIC 1239 No. of Pages 13 genres. Second, 7 out of 50 clusters share the reference to what may be seen as a broadly ‘functional’ or ‘situational’ consumption, in which the salience of the context of reception (e.g. an intimate dinner, a party, relaxing or helping babies sleep) becomes the main cultural aspect. This opens up broader reflections on digital music audiences and music reception, since these crowd-generated music categories seemingly indicate a potential departure from traditional notions of music taste and consumption based on genre affiliations (which are often rooted in particular subcultures). Instead, these categories embrace a culture of music reception based on the ‘situational’ purpose that the music video serves in relation to the context and function of reception. 2. Literature review 2.1. Popular music in the digital world Music today is largely produced, distributed and consumed via digital means (Suhr, 2009; Tepper & Hargittai, 2009). The music industry arguably experienced the breakthrough of digital technologies into the processes of production and, more recently, of distribution and diffusion of music. Since 1999, when Napster first enabled peer-to-peer exchanges of small-size music files free of charge, up to contemporary systems that permit the streaming of large musical databases at a price scale that goes from low to no cost, listening to music has become an intangible, digitally-mediated and increasingly mobile consumption practice (Prior, 2014). A leading role in this context is played by the video streaming platform YouTube. Founded in 2005 as an independent video-sharing website, it was purchased by Google in 2006 and quickly established itself as one of the giants of the social media industry. According to official sources, YouTube had more than 1 billion monthly users in 2015, and an estimated 300 h of video were uploaded every minute.2 Contributions in the literature by Thelwall et al. (2012), Cayari (2011), Green and Burgess (2009) all clearly demonstrate how central music is on YouTube and to the user-created content that is predominant on the platform. In the music industry the traditional broadcast mode of communication in which radio stations and MTV were the main players has now substantially been replaced by an online-based industry. This is shaped by the socio-technical characteristics of digital platforms potentially giving rise to new forms of reception and cultural understandings of musical content. Music videos remain crucial promotional tools for artists and their branding strategies (Vernallis, 2010), but they are now principally shared on platforms like YouTube and Vevo for purposes of viral diffusion over social media. Arguably, YouTube is the centre of this dynamic as a free-to-use, video-centred ecosystem where user-created content (Suhr, 2009; van Dijck, 2009) blends with official music videos. This unprecedented availability of music content in different formats, potentially impacts not only music production, distribution and reception, but also music discovery, exploration and reception (Baek, 2015; Tepper and Hargittai, 2009). As Rimmer points out, “there now exist a range of interactive resources through which Internet users may become (digitally) converted to new or other musical forms” (2012:303). Furthermore, this dynamic introduces a crucial interplay with non-human actors and, in particular, recommendation algorithms (see Hallinan & Striphas, 2014; Striphas, 2015). These algorithms feature prominently on platforms such as Amazon, Last.Fm, YouTube and Spotify and play a key role in shaping contemporary music reception and exploration pathways (Celma, 2010), because they provide users with automated suggestions that influence “various decision-making processes, such as what items to buy, what music to listen to, or what online news to read” (Ricci et al., 2011:1). Flourishing in the e-commerce sector around the early 2000s (Bolton, Katok, & Ockenfels, 2004), recommendation systems work through a variety of methods (see Celma, 2010), normally aggregating similar items and/or users to suggest consumption choices and patterns (Ricci et al., 2011). There is a growing literature in the social sciences discussing the pervasive and invisible power of algorithms over the everyday lives and experiences of Internet users (e.g. Beer, 2009) as well as, more broadly, of citizens (e.g. Cheney-Lippold, 2011). However, so far only few contributions have dealt with the way recommendation systems blend with user interaction (e.g. Striphas, 2015). Despite being a popular topic in computer science, it has to date almost exclusively been addressed from the point of view of performance evaluation (e.g. Celma, 2010; Ricci et al., 2011) and individual user behaviour (e.g. Bolton et al., 2004). 2.2. Music reception and recommendation on YouTube As mentioned above, YouTube features a recommendation system that automatically produces “a ranked list of related videos shown to the user in response to the video that she is currently viewing” (Bendersky et al., 2014: 1). The YouTube user interface shows by default 25 related videos for any video that is being watched. According to recent publications, the current related video suggestion techniques on YouTube are mainly based on collaborative filtering. The principal data source taken into account by the algorithm are patterns of shared viewership. In other words, if many users watch video A right after video B, these two videos are likely to then be ‘related’ (Bendersky et al., 2014). However, the exact functioning of YouTube’s recommendation system is confidential and – like most proprietary algorithms – it may frequently change (Beer, 2009; Rogers, 2013). Less recent contributions in the literature seem to suggest that YouTube’s recommendation system applies a co-view-based ‘behavioural’ logic (Baluja et al., 2008) or one that is ‘syntactical’, based on matching keywords within the M. Airoldi et al. / Poetics xxx (2015) xxx–xxx 3 2 Statistics available at https://www.youtube.com/yt/press/statistics.html [Accessed: 18 May 2015]. Please cite this article in press as: M. Airoldi, et al., Follow the algorithm: An exploratory investigation of music on YouTube, Poetics (2016), http://dx.doi.org/10.1016/j.poetic.2016.05.001PDF Image | Follow the algorithm: An exploratory investigation of music on YouTube
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