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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]

This paper discusses how we intend to take forward the vision of a Bibliography of Life in the ViBRANT project. The underlying principle of the Bibliography is to provide taxonomists and others with a freely accessible bibliography covering the whole of life. Such a bibliography has been achieved for specific study areas within taxonomy, but not for "life" as a whole. The creation of such a comprehensive tool has been hindered by various social and technical issues. The social concerns focus on the willingness of users to contribute to the Bibliography. The technical concerns relate to the architecture required to deliver the Bibliography. These issues are discussed in the paper and approaches to addressing them within the ViBRANT project are described, to demonstrate how we can now seriously consider building a Bibliography of Life. We are particularly interested in the potential of the resulting tool to improve the quality of bibliographic references. Through analysing the large number of references in the Bibliography we will be able to add metadata by resolving known issues such as geographical name variations. This should result in a tool that will assist taxonomists in two ways. Firstly, it will be easier for them to discover relevant literature, especially pre-digital literature; and secondly, it will be easier for them to identify the canonical form for a citation. The paper also covers related issues relevant to building the tool in ViBRANT, including implementation and copyright, with suggestions as to how we could address them. [ABSTRACT FROM AUTHOR]

Many requirements documents are written in natural language (NL). However, with the flexibility of NL comes the risk of introducing unwanted ambiguities in the requirements and misunderstandings between stakeholders. In this paper, we describe an automated approach to identify potentially nocuous ambiguity, which occurs when text is interpreted differently by different readers. We concentrate on anaphoric ambiguity, which occurs when readers may disagree on how pronouns should be interpreted. We describe a number of heuristics, each of which captures information that may lead a reader to favor a particular interpretation of the text. We use these heuristics to build a classifier, which in turn predicts the degree to which particular interpretations are preferred. We collected multiple human judgements on the interpretation of requirements exhibiting anaphoric ambiguity and showed how the distribution of these judgements can be used to assess whether a particular instance of ambiguity is nocuous. Given a requirements document written in natural language, our approach can identify sentences that contain anaphoric ambiguity, and use the classifier to alert the requirements writer of text that runs the risk of misinterpretation. We report on a series of experiments that we conducted to evaluate the performance of the automated system we developed to support our approach. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents. [ABSTRACT FROM AUTHOR]

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.