That’s Not News: Audience Perceptions of “News-ness” and Why It Matters

ABSTRACT

When is a tweet considered news? This study uses an experimental design to isolate two features of a headline shared on Twitter to determine the impact on audience ratings of ‘news-ness.’ We examine how people rate a Twitter post about potential government shutdown depending on: the type of story headline (breaking, exclusive, fact check, opinion), and the source of the story/tweet (Associated Press, MSNBC, Fox News). Results show that headline story type and source separately impact news-ness, with partisanship conditioning the influence of source on news-ness. Moreover, we find that ratings of news-ness mediate these effects on intent to verify tweet content, such that higher ratings of news-ness results in lower intent to verify. We argue that more attention needs to be paid to the central role that perceptions of news-ness plays in driving a range of outcomes in today’s social media environment.

Acknowledgments

The authors would like to thank the Medill School of Journalism at Northwestern University for providing funding for this study.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ IRB approval granted by Northwestern University; July of 2018 (STU00207950).

² To ensure random selection worked, we ran a series of independent sample t-tests among included and excluded Democrats for demographic differences and in our outcome variables. No group differences were significant, suggesting random selection was effective. We also used t-tests to compare participants across the two data collections (September vs. December). We found two significant differences: participants from the December data collection were older (M = 38.46, SD = 11.69) than those in the September collection (M = 36.14, SD = 10.50, t = 4.07, p =.000), and those in the December collection reported higher verification intentions (M = 3.32, SD = 1.83) than the September participants (M = 3.13, SD = 1.68, t = 2.23, p =.03). We return to this limitation in our discussion section.

³ Two additional sources were included in the original experimental design: Buzzfeed News and FactCheck.org. We did not analyze these conditions for this paper, as we focus on the intersection between approach, partisan news sources, and political predispositions. Additional details on these conditions are available from the lead author.

⁴ Due to random assignment into conditions, these numbers are no longer perfectly equivalent.

⁵ In the supplemental appendix, we report analyses using the full sample. The effects are mostly consistent but smaller for the effects of source cues using the full sample.

⁶ We also estimate the same ANOVAs as above with credibility as the dependent variable; reported in the supplemental appendix.

Additional information

Notes on contributors

Stephanie Edgerly

Stephanie Edgerly is an Associate Professor in the Medill School of Journalism at Northwestern University. Her research explores how changes in the media environment impact audience understanding and engagement, with a specific focus on adolescents and young adults.

Emily K. Vraga

Emily K. Vraga is an Associate Professor in the Hubbard School of Journalism and Mass Communication at the University of Minnesota. Her research focuses on how individuals respond to news and information about contentious political, scientific, and health issues, particularly when they encounter disagreement with their views via digital media.