Conference Paper

A Novel Method to Gauge Audience Engagement with Televised Election Debates Through Instant, Nuanced Feedback Elicitation

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ACM Press, New York

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Despite a steep increase in the use of the Internet and handheld computing devices for media consumption, television is still of critical importance for democratic citizenship. Television continues to be the leading source of political information and its relevance has been recognised at policy level. In addition, television keeps evolving technologically and in how it is experienced by viewers. Nonetheless, the ways researchers have measured audience engagement with televised political events in real-time is often limited to small samples of viewers and is based upon a narrow range of responses. In this paper we look at the audience of televised election debates, and propose a new method to gauge the richness and variety of citizens' real-time responses at scale by capturing nuanced, non-intrusive, simple and measurable audience feedback. We report on a paper prototype experiment, in which we used a set of flashcards to test the method in an actual televised election debate scenario. We demonstrate how the method can improve our understanding of viewer responses to the debaters' performances, to the contents in their arguments, and to the debate as media event. We conclude with design guidelines to implement the method on a mass scale in order to measure audience engagement with televised election debates in distributed contexts through audience feedback web and mobile applications.

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De Liddo, Anna; Plüss, Brian; Wilson, Paul (2017): A Novel Method to Gauge Audience Engagement with Televised Election Debates Through Instant, Nuanced Feedback Elicitation. Proceedings of the 8th International Conference on Communities and Technologies. DOI: 10.1145/3083671.3083673. ACM Press, New York. ISBN: 978-1-4503-4854-6. pp. 68-77. Long Papers. Troyes, France. June 26-30, 2017

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Televised election debates, audience feedback, audience research, citizen engagement, collective intelligence, multimedia annotation

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