67,367 research outputs found

    Who is leading the campaign charts? Comparing individual popularity on old and new media

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    Traditionally, election campaigns are covered in the mass media with a strong focus on a limited number of top candidates. The question of this paper is whether this knowledge still holds today, when social media outlets are becoming more popular. Do candidates who dominate the traditional media also dominate the social media? Or can candidates make up for a lack of mass media coverage by attracting attention on Twitter? This study addresses these question by paring Twitter data with traditional media data for the 2014 Belgian elections. Our findings show that the two platforms are indeed strongly related and that candidates with a prominent position in the media are generally also most successful on Twitter. This is not because more popularity on Twitter translates directly into more traditional media coverage, but mainly because largely the same political elite dominates both platforms

    Speaking to twin children: evidence against the "impoverishment" thesis

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    It is often claimed that parents’ talk to twins is less rich than talk to singletons and that this delays their language development. This case study suggests that talk to twins need not be impoverished. We identify highly sophisticated ways in which a mother responds to her 4-year-old twin children, both individually and jointly, as a way of ensuring an inclusive interactional environment. She uses gaze to demonstrate concurrent recipiency in response to simultaneous competition for attention from both children, and we see how the twins constantly monitor the ongoing interaction in order to appropriately position their own contributions to talk. In conclusion, we argue for the need to take twins’ interactional abilities into account when drawing linguistic comparisons between twins and singletons. Data are in Australian English

    Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate

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    With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter we use graph-theoretic methods to analyse the Twitter debate surrounding NHS Englands controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium

    Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue

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    Research on human spoken language has shown that speech plays an important role in identifying speaker personality traits. In this work, we propose an approach for identifying speaker personality traits using overlap dynamics in multiparty spoken dialogues. We first define a set of novel features representing the overlap dynamics of each speaker. We then investigate the impact of speaker personality traits on these features using ANOVA tests. We find that features of overlap dynamics significantly vary for speakers with different levels of both Extraversion and Conscientiousness. Finally, we find that classifiers using only overlap dynamics features outperform random guessing in identifying Extraversion and Agreeableness, and that the improvements are statistically significant.Comment: Proceedings Interspeech 2019, Graz, Austria, Septembe
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