{"id":"W4409487036","doi":"10.1177/20552076251335717","title":"Breathing together: A global hashtag analysis of #LungHealth on platform X (formerly Twitter)","year":2025,"lang":"en","type":"article","venue":"Digital Health","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Outreach; Context (archaeology); Popularity; Arabic; Scale (ratio); Medicine; Geography; Medical education; Political science; Computer science; World Wide Web; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004415109,0.0004017391,0.0002156906,0.002802105,0.0004211461,0.0007251701,0.0001751347,0.0003782279,0.002346325],"category_scores_gemma":[0.0016544,0.00009183928,0.0003739193,0.00228057,0.0002258929,0.0009634509,0.000797039,0.0003872191,0.001705614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113648,"about_ca_system_score_gemma":0.0002579786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007364152,"about_ca_topic_score_gemma":0.01355973,"domain_scores_codex":[0.9997109,0.00005364534,0.00002716833,0.00005633351,0.00008817638,0.00006378676],"domain_scores_gemma":[0.9986737,0.0004504067,0.0002819808,0.00010806,0.0003383864,0.0001474658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007188687,0.0001725775,0.7818298,0.00106389,0.0001624973,0.001422433,0.008745137,0.00136133,0.02673639,0.001445546,0.03572753,0.1406138],"study_design_scores_gemma":[0.00001329293,0.0003362735,0.9403387,0.000090104,0.00007808656,0.0008279782,0.009317088,0.009338113,0.004997683,0.0005096703,0.03409779,0.0000552436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583139,0.0003069392,0.001811037,0.0004801479,0.0001279265,0.0001497791,0.03190313,0.0005314451,0.006375627],"genre_scores_gemma":[0.9558517,0.0002362425,0.005016074,0.0002059184,0.0001144214,0.0001457176,0.03232099,0.0001053167,0.006003654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007364152,"threshold_uncertainty_score":0.0146426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08769934658391276,"score_gpt":0.4482999126651819,"score_spread":0.3606005660812691,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}