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G Model POETIC 1239 No. of Pages 13 (]GIF$DT)2_.giF[ M. Airoldi et al. / Poetics xxx (2015) xxx–xxx 9 Fig. 2. Music clusters on the semantic map, positioned according to the types of words occurring in video titles. Background Music’ (y = 1.6), ‘Guitar Tutorial/For Musicians’ (y = 0.6), ‘Gospel/Worship Music’ (y = 0.3), ‘Flute/Piano Cover/ Tutorial’ (y = 0.2). In all these clusters, what is particularly evident is the salience of the ‘situational purpose’ of the music (or sounds). ‘Sounds of Nature’ and ‘Hair Dryer Sound’ music videos are always portrayed ‘for relaxing’ or ‘for sleeping’; the same (plus ‘meditation’) applies to ‘Relaxing Background Music’. ‘Music for Babies’ and ‘Gospel/Worship Music’, respectively, include music for keeping babies calm and for religious worship, whilst the remaining two clusters are collections of tutorials and videos ‘for musicians’. The higher the y-value, the more references to the context of music reception in the titles of the videos, and the more a specific word becomes frequent: ‘hour(s)’. Therefore, the value of this ‘situational music’ seems to depend more on its duration – which of course has to be long enough to musically support what listeners are doing at the time – than on its perceived quality or on the reputation of the performers. Finally, 8 out of 50 clusters are particularly close to the origin of the axes: ‘Teen Pop’, ‘Pop Hits’, ‘Alternative 80s/90s’, ‘Dance/Trance’, ‘Soul/Singers/Orchestra’, ‘Trap Music’, ‘Teen Pop Fandom’ and ‘Arabic Pop’. Clusters based on pop Charts – that are normally neither stylistically homogeneous (Lena & Peterson, 2008: 700) nor rooted in a specific ‘milieu’ (Webb, 2007) of production – all reside in this interval. Thanks to the semantic map shown above, we have been able to devise an analytic scheme to interpret and compare the different logics of similarity emerging from the videos’ discursive frames (Goffman, 1974). Although the binary oppositions ‘genre/cross-genre’ and ‘context of production/context of reception’ are not the only suitable heuristic tools for this purpose, they proved very useful to show the semantic discrepancy that we outlined. Our analysis shows that the retrieved music clusters are not meaningless agglomerations of videos. On the contrary, there seems to exist an underlying cultural logic of similarity within each cluster that is produced by the technologically-mediated and aggregated practices of usages by listeners and uploaders. The social logic of aggregation pertaining to these clusters is particularly evident in the case of ‘Ethiopia/South Sudan Music’, which includes 301 music videos. The majority of these videos (137) explicitly refer to the Ethiopian musical scene, whereas 26 mention South Sudan and 10 are spontaneously defined as ‘Nuer Music’. Nuer are a tribal ethnic group on the border of Ethiopia and South Sudan, also studied by the anthropologist Evans-Pritchard (1940). The strong connections between these three geographically and culturally close traditions are evident in the structure of the related-videos network, which in this case clearly materialises a local musical milieu. The complex interplay between the culturally-informed practices of music makers and listeners acting on the platform (e.g. building a genre-based playlist, promoting an artist’s official YouTube channel, uploading one’s favourite songs or, simply, listening to music) is translated and simplified by the recommender system’s technical logic. The result is that a small cluster in a network of 22,141 nodes reproduces a coherent miniature of a very specific musical culture. There are no Ethiopian songs in more ‘western’ music clusters or even among Ugandan music videos, and vice versa. In particular, the degree centrality measure of this latter cluster in the network analysis is actually 0. This means that there are no connections with any other cluster. This last example that is, a culturally and stylistically-coherent cluster composed by music videos defined by various textual frames (Ethiopian music, Nuer music, South Sudan music) supports our assumption of the predominance of a co-view-based ‘behavioural’ logic over a ‘syntactical’ one in the current functioning of the YouTube algorithm (see Section 2.2). Otherwise, the prevalence of a ‘natural language processing’ approach based on video metadata would have undermined the results of our text analysis. 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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