Data, Sense, and Sensibility: How Data Journalism Style Shapes Interactivity

Abstract

Data journalism (DJ) seeks to enhance audience comprehension and engagement by integrating statistical information, diverse sources, data visualizations, and journalistic style. However, the way DJ stories are framed—whether through an analytic approach that prioritizes precision or an affective approach that emphasizes emotional engagement—may shape audience interactivity in distinct ways. This study examines how journalistic style influences audience engagement in DJ by analyzing 6,400 New York Times (NYT) stories and 785,883 comments from 2014 to 2022. Using computational text analysis and mediation modeling, we assess how DJ stories balance analytic and affective elements and how these stylistic choices impact user interaction. The findings indicate that DJ stories tend to adopt a more affective and less analytic style compared to traditional journalism. While affective framing increases comment volume, it is negatively associated with conversation depth. In contrast, analytic framing and static visualizations contribute to deeper discussions but attract fewer initial comments. DJ stories, overall, generate fewer comments than traditional stories, yet when comments do appear, they are more likely to develop into conversations. These results suggest a trade-off in DJ: an affective approach fosters broader engagement, while an analytic approach and static visualizations support in-depth discussions. This study highlights the evolving role of DJ in shaping audience interactivity and underscores the need for news organizations to balance emotional resonance with analytical clarity to foster both engagement and substantive discourse.