Firearm Violence and Health in Policymaker Discourse: Mixed Methods Social Media Analysis
Notice bibliographique
Résumé
Background: Since 2019, firearm violence has remained the leading cause of death for US children and adolescents aged 1-19 years. This crisis has spurred action from policymakers, health professionals, and advocates. However, political polarization has contributed to divergent views on the causes and appropriate responses to firearm violence. Communication by elected officials, especially on social media, plays a critical role in shaping public opinion and policy agendas. Understanding how state policymakers discuss firearm violence, including the use of causal blame, calls to action, and health-related narratives, can inform more effective public health strategies. Objective: This study aimed to examine how Pennsylvania state legislators discuss firearms and firearm violence on social media and assess the extent to which their messaging aligns with public health perspectives. Methods: We conducted a 2-phase mixed methods analysis of X (formerly known as Twitter; X Corp) posts by Pennsylvania state legislators from May 27, 2017, to July 26, 2022. Posts were grouped into 3 time periods surrounding the Tree of Life Synagogue mass shooting in Pittsburgh. Using a Boolean search strategy, we identified 4573 posts related to firearms and firearm violence. After removing reposts and non-English content, we randomly sampled 1491 (32.6%) original posts authored by 152 unique legislators. Posts were coded using a structured codebook based on the Multiple Streams Framework to capture rhetorical framing, causal blame, and policy content. Interrater reliability was high (Holsti coefficient >0.8). We used chi-square tests and multivariable logistic regression to assess associations between rhetorical elements and policy mentions, adjusting for time period. Results: Mass shootings were the most frequently referenced category of firearm violence, peaking after the Tree of Life shooting (22/43, 51% vs 91/118, 77.1% vs 140/220, 63.6%; P=.004), while firearm suicide was rarely discussed. Posts using advocacy frames were nearly 5 times more likely to mention policy (adjusted odds ratio [aOR] 4.67, 95% CI 3.55-6.16), whereas those referencing mass shootings (aOR 0.54, 95% CI 0.37-0.77) or emotional appeals (aOR 0.53, 95% CI 0.40-0.69) were significantly less likely to do so. Most posts used general advocacy (aOR 2.97, 95% CI 2.13-4.13) and vague blame (aOR 8.26, 95% CI 6.02-11.35), resulting in nonspecific policy suggestions. Posts that attributed blame to firearm access were strongly associated with specific policy proposals (aOR 6.37, 95% CI 4.29-9.47) and inversely associated with general policy mentions (aOR 0.26, 95% CI 0.17-0.42). Only 9.4% (133/1422) of posts used health frames; when present, they more often referenced physical consequences (58/133, 43.6% vs 216/1358, 15.9%; P<.001). Conclusions: Pennsylvania legislators primarily focused on mass shootings and relied on emotional or symbolic language without proposing specific policies. Health frames were rare and typically focused on consequences rather than prevention. Findings highlight an opportunity to support policymakers with health-informed messaging strategies to promote actionable firearm violence prevention policies, particularly those addressing prevention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».