Clickbait news and algorithmic curation: A game theory framework of the relation between journalism, users, and platforms

Algorithmic curation of social media platforms is considered to create a clickbait media environment. Although clickbait practices can be risky especially for legacy news outlets, clickbait is widely applied. We conceptualize clickbait content supply as a revision game with an unknown threshold. Combining supervised machine learning with time series analysis of Facebook posts and Twitter messages of 37 German legacy news outlets over 54 months, we observe outlets' behavior following algorithm changes. Results reveal (1) an infrequent use of clickbait with few heavier-using outlets and (2) turning points of clickbait performance as clickbait supply and user interaction form a reversed U-shaped relationship. News outlets (3) collectively adjust toward an industry clickbait standard. While we (4) cannot prove that algorithmic curation increases clickbait, (5) Facebook's regulative intervention to decrease clickbait disperses heterogeneous tendencies in clickbait supply. We contribute to an understanding of editorial decision-making in competitive environments facing platforms' regulative intervention.