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In the context of deteriorating relations with 'Western' states, Russia's state-funded international broadcasters are often understood as malign propaganda rather than as agents of soft power. Subsequently, there is a major credibility gap between how Russian state media represents itself to the world and how it is actually perceived by overseas publics. However, based on the study of RT's coverage of the Russian hosted FIFA 2018 World Cup and the audience reactions this prompted, we find that this credibility gap was partially bridged. By analysing over 700 articles published by RT, alongside social media and focus group research, we find that RT's World Cup coverage created an unusually positive vision of Russia that appealed to international audiences. Our study demonstrates how state-funded international broadcaster coverage of sports mega-events can generate a soft power effect with audiences, even when the host state – such as Russia – has a poor international reputation. [ABSTRACT FROM AUTHOR]

The Russian state-funded international broadcaster RT (formerly Russia Today) has attracted much attention as a purveyor of Russian propaganda. To date, most studies of RT have focused on its broadcast, website, and social media content, with little research on its audiences. Through a data-driven application of network science and other computational methods, we address this gap to provide insight into the demographics and interests of RT's Twitter followers, as well as how they engage with RT. Building upon recent studies of Russian state-sponsored media, we report three main results. First, we find that most of RT's Twitter followers only very rarely engage with its content and tend to be exposed to RT's content alongside other mainstream news channels. This indicates that RT is not a central part of their online news media environment. Second, using probabilistic computational methods, we show that followers of RT are slightly more likely to be older and male than average Twitter users, and they are far more likely to be bots. Third, we identify thirty-five distinct audience segments, which vary in terms of their nationality, languages, and interests. This audience segmentation reveals the considerable heterogeneity of RT's Twitter followers. Accordingly, we conclude that generalizations about RT's audience based on analyses of RT's media content, or on vocal minorities among its wider audiences, are unhelpful and limit our understanding of RT and its appeal to international audiences. [ABSTRACT FROM AUTHOR]

Throughout 2017, the Russian state broadcaster, RT (formerly Russia Today), commemorated the centenary of the 1917 revolution with a social media re-enactment. Centred on Twitter, the 1917LIVE project involved over 90 revolution-era characters tweeting in real time as if the 1917 revolution was happening live on social media. This article is based on an analysis of a sample of tweets by users who engaged with 1917LIVE, alongside focus group discussions with its followers. We argue that a cultural studies perspective can shed important light on the political significance of RT's social media re-enactment in ways that current studies of public diplomacy as a soft power resource often fail to do. It can advance soft power theory by offering a more nuanced, dynamic analysis of how state media mobilise, and how audiences engage with, social media re-enactments as commemorative events. We find that rather than promoting a unitary propagandistic narrative about Russia, 1917LIVE served instead to soften attitudes towards RT itself – encouraging audiences to view RT as an educator and entertainer as well as a news broadcaster – normalising its presence as a Russian public diplomacy resource in the international news media landscape. Our analysis of audience interactions with and interpretations of 1917LIVE affords insights into how the 1917 re-enactment worked as didactic entertainment eliciting affective identification with the characters of the revolution. Such public diplomacy projects contribute in the short term to a strengthening of the engagement required to create longer-term soft power effects. [ABSTRACT FROM AUTHOR]

This paper pays attention to the immense and febrile field of digital image files which picture the smart city as they circulate on the social media platform Twitter. The paper considers tweeted images as an affective field in which flow and colour are especially generative. This luminescent field is territorialised into different, emergent forms of becoming 'smart'. The paper identifies these territorialisations in two ways: firstly, by using the data visualisation software ImagePlot to create a visualisation of 9030 tweeted images related to smart cities; and secondly, by responding to the affective pushes of the image files thus visualised. It identifies two colours and three ways of affectively becoming smart: participating in smart, learning about smart, and anticipating smart, which are enacted with different distributions of mostly orange and blue images. The paper thus argues that debates about the power relations embedded in the smart city should consider the particular affective enactment of being smart that happens via social media. More generally, the paper concludes that geographers must pay more attention to the diverse and productive vitalities of social media platforms in urban life and that this will require experiment with methods that are responsive to specific digital qualities. [ABSTRACT FROM AUTHOR]

Stylistic composition is a creative musical activity, in which students as well as renowned composers write according to the style of another composer or period. We describe and evaluate two computational models of stylistic composition, called Racchman-Oct2010 (random constrained chain of Markovian nodes, October 2010) and Racchmaninof-Oct2010 (Racchman with inheritance of form). The former is a constrained Markov model, and the latter embeds this model in an analogy-based design system. Racchmaninof-Oct2010 applies a pattern discovery algorithm called SIACT and a perceptually validated formula for rating pattern importance, to guide the generation of a new target design from an existing source design. A listening study is reported concerning human judgments of music excerpts that are, to varying degrees, in the style of mazurkas by Frédéric Chopin (1810–1849). The listening study acts as an evaluation of the two computational models and a third, benchmark system, called Experiments in Musical Intelligence. Judges' responses indicate that some aspects of musical style, such as phrasing and rhythm, are being modeled effectively by our algorithms. Judgments are also used to identify areas for future improvements. We discuss the broader implications of this work for the fields of engineering and design, where there is potential to make use of our models of hierarchical repetitive structure. [ABSTRACT FROM PUBLISHER]