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Record W4414622909 · doi:10.7302/27022

The Scientist?s (Social Media) Playbook: A Triangulated Approach to Understanding Scientists? Self-Presentation, Audience Norms, and the Public Communication of Science

2025· dissertation· en· W4414622909 on OpenAlexaboutno aff
Annie L. Zhang

Bibliographic record

VenueDeep Blue (University of Michigan) · 2025
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
Fundersnot available
KeywordsScience communicationSocial mediaIdentity (music)Key (lock)Prosocial behaviorPublic engagementTechnical communicationScholarly communication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.005
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.187
GPT teacher head0.350
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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