News Organizations’ Selective Link Sharing as Gatekeeping

A Structural Topic Model Approach

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 and illustrates how it 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 different topic distribution between news websites and Twitter, significantly revoking 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, which relies on journalistic values and norms.