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The increasing relevance of social networking platforms is accompanied by a growing number of studies using digital trace data. However, most studies still lack further understanding of the data-generating process. This analytical gap can be directly attributed to the prevalence of quantitative approaches, as only qualitative work is able to generate these insights. The broad methodological toolset of Discourse Network Analysis addresses this shortcoming as it combines both qualitative and quantitative approaches. The present study therefore employs Discourse Network Analysis in order to (1) determine different user groups’ varying role as senders and recipients of targeted online conversations, (2) identify and compare Twitter users’ (simultaneous) reference to different forms of conversational Twitter content, and to (3) asses the motivation of @message authors to direct particular tweets at particular user groups. To this end, this study analyzes @messages during the BBC program ‘Question Time’ on 2nd of June 2017—the final media encounter of Prime Minister Theresa May and Jeremy Corbyn in the context of the 2017 UK election campaign. We draw on the theoretical background of Maarten Hajer’s discourse coalitions approach in order to investigate the preconditions for the formation of discourse coalitions in new and emerging virtual discourse arenas. Thus, our work not only mirrors the focus in existing literature on Twitter usage during high-profile political media events, but also emphasizes Twitter’s unique features for interactive exchange. This article identifies different forms of meta-talk and policy issues, which vary in both their general popularity with Twitter users as well as their interconnectedness. Furthermore, our analysis uncovers the motivation behind the decisions of @message authors to send particular @messages to certain groups of Twitter users. Finally, we could establish that media events only temporarily affect the topical foci of @message authors.

Discourse Networks and Dual Screening: Analyzing Roles, Content and Motivations in Political Twitter Conversations