Anticipating Attention: On the Predictability of News Headline Tests. Issue 4 (10th May 2022)

• Record Type: Journal Article
• Authors: Hagar, Nick; Diakopoulos, Nicholas; DeWilde, Burton
• Is Part Of: Digital journalism. Volume 10:Issue 4(2022)
• Page Start: 647
• Page End: 668
• Publication Date: 2022-05-10
• Subjects: Digital journalism -- headline writing -- news attention -- text analysis -- news values -- news production -- computational methods; Online journalism -- Periodicals; 070.40285
• DOI: 10.1080/21670811.2021.1984266
• Languages: English
• ISSNs: 2167-0811

Abstract

Abstract: Headlines play an important role in both news audiences' attention decisions online and in news organizations' efforts to attract that attention. A large body of research focuses on developing generally applicable heuristics for more effective headline writing. In this work, we measure the importance of a number of theoretically motivated textual features to headline performance. Using a corpus of hundreds of thousands of headline A/B tests run by hundreds of news publishers, we develop and evaluate a machine-learned model to predict headline testing outcomes. We find that the model exhibits modest performance above baseline and further estimate an empirical upper bound for such content-based prediction in this domain, indicating an important role for non-content-based factors in test outcomes. Together, these results suggest that any particular headline writing approach has only a marginal impact, and that understanding reader behavior and headline context are key to predicting news attention decisions.