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Open Access News Organizations’ Selective Link Sharing as Gatekeeping : A Structural Topic Model Approach

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Abstract

To disseminate their stories efficiently via social media, news organizations make decisions that resemble traditional editorial decisions. However, the decisions for social media may deviate from traditional ones because they are often made outside the newsroom and guided by audience metrics. This study focuses on selective link sharing as quasi-gatekeeping on Twitter ‐ conditioning a link sharing decision about news content. It illustrates how selective link sharing resembles and deviates from gatekeeping for the publication of news stories. Using a computational data collection method and a machine learning technique called Structural Topic Model (STM), this study shows that selective link sharing generates a different topic distribution between news websites and Twitter and thus significantly revokes the specialty of news organizations. This finding implies that emergent logic, which governs news organizations’ decisions for social media, can undermine the provision of diverse news.
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Keywords: Structural Topic Model; gatekeeping; selective link sharing

Document Type: Research Article

Publication date: October 1, 2019

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  • Computational Communication Research (CCR) is a peer reviewed and open-access journal focusing on development and application of computational methods in communication science. Computational Methods are of increasing importance and prominence in the field of Communication Science. CCR aims to provide a central home for communication scientists with an interest in and focus on computational methods — a place to read and publish the cutting edge work in this growing subfield.
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