{"id":"W2981683958","doi":"10.2196/14137","title":"The Reach of the “Don’t Fry Day” Twitter Campaign: Content Analysis","year":2019,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Content analysis; Context (archaeology); Categorization; Social media; Promotion (chess); Descriptive statistics; Theme (computing); Psychology; Advertising; Political science; Sociology; Geography; World Wide Web; Computer science; Business; Social science; Artificial intelligence; Politics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006041649,0.00006998191,0.0002320075,0.00006083131,0.0004189891,0.00001870378,0.0004562451,0.0001559082,0.0001938729],"category_scores_gemma":[0.000797736,0.00004260992,0.0001351091,0.0007130546,0.0004794466,0.00005274912,0.00004645116,0.0001740432,0.00008819327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001349997,"about_ca_system_score_gemma":0.0003640617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004202459,"about_ca_topic_score_gemma":0.003477543,"domain_scores_codex":[0.9979513,0.0009713103,0.0003072168,0.0001506271,0.0003004699,0.0003191024],"domain_scores_gemma":[0.9967374,0.00239711,0.0002576395,0.0003997593,0.0001443995,0.00006374245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006038625,0.00002245653,0.94612,0.00001316578,0.00007699784,3.983363e-7,0.04176142,0.000001502961,0.00004455438,0.006946373,0.004141329,0.0008657513],"study_design_scores_gemma":[0.0001494172,0.00001020582,0.7568009,0.000009232018,0.00009033476,0.000001923981,0.0290917,0.00003511608,0.0001495665,0.0007687543,0.212808,0.00008480383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570912,0.0002261987,0.00001048327,0.03353176,0.003715083,0.001116487,0.000001350433,0.00002117823,0.00428634],"genre_scores_gemma":[0.997949,0.00005178918,0.00001739725,0.0008866016,0.0001688529,0.0003731371,0.000002256856,0.00000579878,0.000545171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2086667,"threshold_uncertainty_score":0.635289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.102462931105269,"score_gpt":0.3929311074357709,"score_spread":0.2904681763305019,"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."}}