https://orcid.org/0000-0002-6135-6743
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ABSTRACT
Contention over COVID-19 is only a recent example of increasing social division around science in the U.S. Many blame these divisions on actors who have strategically sowed doubt and distrust around expert supported positions and policies. However, this overlooks how scientists have fueled narratives of social and political conflict around science. This study explores how science influencers on social media have used group identity language in ways that may perpetuate narratives of intergroup conflict around science. Using computer-assisted content analytic methods, we examine how science influencers’ use of group identity language has changed in response to recent events (Trump presidency, COVID-19 pandemic) and across different social media platforms (Twitter, Facebook, Instagram). While there are slight increases in group identity language between 2016 and 2021, different patterns across platforms suggest that science influencers use different platforms to perform multiple roles of engaging diverse audiences, building ingroup solidarity, and defending against outgroup criticism.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, SC, upon reasonable request.
Supplementary material
Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10584609.2023.2201174
Notes
1. It is possible to share a post through Instagram’s “Story” feature. However, as we are looking at actual posting behavior instead of Stories, we consider these to be more independent.
Additional information
Notes on contributors
Sedona Chinn
Sedona Chinn is an assistant professor at the University of Wisconsin-Madison in the Department of Life Sciences Communication. Her research examines attitudes toward science and expertise in contemporary media environments. She draws on experimental, survey, content analytic, and computational approaches to investigate the social and psychological sources of misinformation and mistrust in scientific spaces.
Dan Hiaeshutter-Rice
Dan Hiaeshutter-Rice is an assistant professor in the Department of Advertising and Public Relations at Michigan State University. He studies biases in political information production and consumption. His work uses experiments, content analysis, and computational methods to understand the role that communication platforms play in the information ecosystem.
Kaiping Chen
Kaiping Chen is an assistant professor in Computational Communication at the Department of Life Sciences Communication from University of Wisconsin-Madison. Her research uses data science and machine learning methods as well as interviews to study to what extent digital media and technologies hold politicians accountable for public well-being and how deliberative designs improve the quality of public discourse and mitigate misinformation and misperception. Her works have been published in flagship journals across disciplines such as the American Political Science Review, Journal of Communication, and the Proceedings of the National Academy of Sciences (PNAS).