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G Model POETIC 1239 No. of Pages 13 10 M. Airoldi et al. / Poetics xxx (2015) xxx–xxx (]GIF$DT)3_.giF[ 4.3. Inter-cluster associations Fig. 3. The network of associations among clusters. To complete our exploration of music on YouTube, we examined the clustered network. This network is derived from the community structure of our original related videos network: nodes are now clusters of strongly related videos, while links among clusters correspond to the sum of the links among videos belonging to different clusters. As a result of our interpretation of the clusters as ‘crowd-generated music categories’, the weight of an edge linking two clusters can be seen as an indicator of the ‘relatedness’ between two categories. The clustered network is composed of 50 nodes (the clusters) and 463 edges, as shown in Fig. 3.10 The shape of the visualised network is computed with Gephi11 using the OpenOrd algorithm, designed to highlight clusters. The strongest connections in this network tend to link clusters characterised by similar musical content (e.g. ‘Pop Hits’ with ‘Teen Pop’; ‘Relaxing Background Music’ with ‘Epic Music/Soundtrack’). Furthermore, the network is roughly divided into two main ‘meta-clusters’, one of which mostly contains clusters largely referring to conventional music categories (e.g. pop, rock, country), while the other is characterised by what may be seen as a prevalently ‘situational’ logic (e.g. relaxing, background, meditation, music for babies). Since the probability of clusters being connected is affected by the size of the same clusters, Table 2 lists the ten strongest associations between clusters divided by the size of the source cluster. Each row thus provides the strongest relative associations in network. Again, we can conclude that connections between clusters show a substantially high rate of stylistic contiguity. 5. Discussion, digital music between ‘genre’ and ‘situation’ The analysis of the network associations within a large sample of YouTube music videos has shown how the users’ aggregated reception practices – which we assumed as determinant in the relatedness between two music videos in combination with the recommender algorithm – produce groupings that can be interpreted as crowd-generated music categories (see Striphas, 2015). These groupings are categories that emerge from the interplay of platform “affordances” (Baym & Boyd, 2012) and the reception patterns of music communities (see Fabbri, 1982). Through a content analysis of videos’ titles and the interpretation of the resulting ‘semantic map’, we also provide evidentiary support on how a peculiar ‘logic of similarity’ seems to characterise these crowd-generated music categories and their content in an ideal continuum that goes – on the one hand – from conventional genre affiliations to the presence of specific cross-genre stylistic traits and – on the other hand – from the semantic prevalence of the context of music production to that of music reception. The interpretation of our clusters as crowd-generated music categories seems legitimate considering that Franco Fabbri defines music genres as sets “of musical events (real or possible) whose course is governed by a definite set of socially accepted rules”, which are shared by a given “community” of listeners (1982:52–53). Similarly, Simon Frith stated that genre “is not determined by the form or style of a text itself but by the audience’s perception of its style and meaning” (1996:94). Put differently, in coherence with these authors that support the idea of the eminently social character of music classification 10 The size of the bubbles is proportional to the number of videos that pertain to the cluster. The thicker the link, the higher the edge weight. 11 See TD$DIFF[91_FF]][76_TD$DIhttps://gephi.org/ [Accessed: 12 June 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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