{"id":"W4377009227","doi":"10.30699/fhi.v12i0.398","title":"COVID-19 Information Dissemination Via Social Media: Content Analysis of Instagram Posts During the COVID-19 Outbreak","year":2023,"lang":"en","type":"article","venue":"Frontiers in Health Informatics","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Kerman University of Medical Sciences","keywords":"Social media; Content analysis; Information Dissemination; Public health; Joke; Descriptive statistics; Coronavirus disease 2019 (COVID-19); Coronavirus; Political science; Psychology; Medicine; Sociology; Social science; World Wide Web; Computer science; Nursing; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005297392,0.0001659443,0.0004661447,0.001743977,0.001236854,0.0001412033,0.000425451,0.0001898818,0.00008545251],"category_scores_gemma":[0.007927317,0.0001389058,0.000145274,0.004455026,0.0003082442,0.001922049,0.00008214959,0.0002536699,0.00002532708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001955105,"about_ca_system_score_gemma":0.001847815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00210502,"about_ca_topic_score_gemma":0.002201406,"domain_scores_codex":[0.9959129,0.0002866828,0.001993061,0.00007541489,0.001137097,0.0005948559],"domain_scores_gemma":[0.9969088,0.0004992163,0.001440011,0.0002295253,0.0001992647,0.0007231349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000600683,0.00002313975,0.01037334,0.0008534426,0.0001078749,4.98726e-7,0.9124321,0.0038481,3.770834e-7,0.003033131,0.04802263,0.02124527],"study_design_scores_gemma":[0.001314498,0.00004751075,0.08472347,0.00004043341,0.00008857519,0.000001790625,0.6830741,0.07046474,0.000005048128,0.000646074,0.1592623,0.0003314447],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.643106,0.0002168498,0.2260859,0.1039563,0.005546724,0.005665412,0.001563533,0.001181253,0.01267793],"genre_scores_gemma":[0.9850293,0.0009036441,0.00100005,0.01196254,0.00008013906,0.00003013993,0.0008069747,0.000009269314,0.0001779428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3419232,"threshold_uncertainty_score":0.9513004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06908424431772228,"score_gpt":0.3816734777713052,"score_spread":0.3125892334535829,"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."}}