The New Yellow Journalism: Examining the Algorithmic Turn in News Organizations’ Social Media Information Practice through the Lens of Cultural Time Orientation
To address the complex challenges posed by increasingly fast information exchange in social media networks and declining advertising revenue in the digital era, news organizations are turning to software to automate online engagement. To date, there has been little study of whether algorithmic social media solutions used by news organizations are able to replicate the nuances of culturally informed human judgment. Using a novel combination of the critical incident technique, network analysis, and a new interpretive method—the Time Analytic Framework for Information Practice—this dissertation explores the effects of cultural time orientation on the social media activity of three culturally distinct news organizations before and after automation.
The present study investigates how cultural time orientation may exacerbate or mitigate the effects of the algorithmic turn on news organization information practice by examining cases in which tweet prioritization appears to have violated reader expectations. Findings suggest that the three methods employed by the news organizations to automate the information practice previously conducted by social media managers reflect the news organizations’ cultural time orientations. Further, case studies of persistent tweets in each social media network reveal the emergence of a new form of yellow journalism—algorithmic sensationalism—arising from information practices that disproportionately amplify inflammatory content and lack a mechanism for applying timely human judgment.