The Scientist?s (Social Media) Playbook: A Triangulated Approach to Understanding Scientists? Self-Presentation, Audience Norms, and the Public Communication of Science
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».