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Sparked by a collaboration between academic researchers and science media professionals, this study sought to test three commonly used headline formats that vary based on whether (and, if so, how) important information is left out of a headline to encourage participants to read the corresponding article; these formats are traditionally-formatted headlines, forward-referencing headlines, and question-based headlines. Although headline format did not influence story selection or engagement, it did influence participants evaluations of both the headline’s and the story’s credibility (question-based headlines were viewed as the least credible). Moreover, individuals’ science curiosity and political views predicted their engagement with environmental stories as well as their views about the credibility of the headline and story. Thus, headline formats appear to play a significant role in audience’s perceptions of online news stories, and science news professionals ought to consider the effects different formats have on readers.
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Anticipating Attention: On the Predictability of News Headline Tests
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.
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- Award ID(s):
- 1717330
- PAR ID:
- 10301845
- Date Published:
- Journal Name:
- Digital Journalism
- ISSN:
- 2167-0811
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/21670811.2021.1984266
