The Scientist?s (Social Media) Playbook: A Triangulated Approach to Understanding Scientists? Self-Presentation, Audience Norms, and the Public Communication of Science
Bibliographic record
Abstract
Tensions between science, scientists, and the public have been brought to the forefront in recent decades, with increasing politicization and polarization around scientific issues (e.g., climate change, vaccines, and the COVID-19 pandemic) that have undermined expert consensus and prosocial behavior. Efforts to understand and address these tensions are thus critical for promoting a healthy, informed society. This dissertation argues that amidst our changing information environment and media landscape, social media have become pivotal spaces poised to impact these tensions and relationships more broadly, offering both new opportunities and challenges that have influenced our understanding of the public communication of science. As scientists, science communication actors, and public audiences increasingly engage with each other online, new questions have emerged about (scientific) credibility, identity, visibility, and audience engagement. Two key phenomena undergird this work. First, individual scientists have emerged as key communicators in these spaces, creating and sharing content that often blends their scientific expertise with their personal identity and goals. Second, audiences are turning to social media more frequently to receive and engage with scientific information, and thus with scientists themselves. Yet the implications of these dynamics remain under-explored: How do scientists manage their public-facing identities on social media? What do audiences expect from scientists’ online communication? And do these performances matter for public trust and engagement? To address these questions, this dissertation presents data from a triangulated, mixed-methods approach, anchored against three key communication components from which these tensions might be addressed: the messenger, the audience, and the message. Study 1 draws on interviews with 24 highly visible scientists from the U.S., Canada, and Europe, across TikTok, Instagram, and X, to examine how they construct and manage their self-presentation on these platforms. Findings reveal how scientists strive for authenticity, manage competing norms, and strategically humanize themselves to build scientific trust and credibility. Study 2 analyzes a two-wave national panel survey (N = 878) to offer an exploratory examination into how audiences’ observation of science content on social media might influence their normative expectations of scientists’ communication behaviors and, in turn, their trust in and willingness to be vulnerable to scientists. Study 3 employs an online experiment (N = 1,843) to examine the effects of scientists’ self-presentation strategies (e.g., sharing successes vs. failures) on audience perceptions and support for science. Across all three studies, this dissertation foregrounds self-presentation as a central aspect of science communication on social media. In doing so, it contributes new insight into how social media are reshaping the relational work of science communication, therefore offering both a more nuanced understanding of the changing science communication landscape and a foundation forward for promoting stronger and more meaningful relationships between science, scientists, and society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".