Somebody’s poisoned the water hole! Advancing social media data collection principles to account for the effects of AI slop on public relations scholarship

Public relations researchers have grappled with social media data for more than two decades. While the field has made considerable strides in using social media data to test, refine, and develop theory, PR scholars have been slow to collect and analyze social media data at scale compared to other social sciences. To date, most social media scholarship in PR has taken a functionalist approach to organizations’ use of social media to build relationships with various publics and stakeholders. But this perspective fails to account for the breadth of conversations that occur between and among organizations and publics. Even more concerning is the rapid growth of content and accounts that utilize artificial intelligence (AI) that pollute the social media data environment – what we refer to as AI slop. This project contributes to social media scholarship in PR by advancing three principles of social media data collection that mitigate the potential effects of AI slop. We articulate the theoretical and practical importance of adopting these principles for any social media data collection efforts moving forward. • .Defines and establishes the prominence and potential effects of AI slop in social media research • Advances data collection principles for PR scholars to adopt in future research that relies on social media data. • Discusses theoretical and practical significance of appropriate social media data collection procedures in the era of generative AI.