{"id":"W3083628402","doi":"10.2196/18273","title":"Exploring Eating Disorder Topics on Twitter: Machine Learning Approach","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Mental Health via Writing","field":"Psychology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Complementary and Integrative Health; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Machine learning; Artificial intelligence; Computer science; Social media; Support vector machine; Naive Bayes classifier; Convolutional neural network; Classifier (UML); Topic model; Natural language processing; Data science; Information retrieval; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001558379,0.0009703486,0.0006177359,0.006818783,0.0008672622,0.00138642,0.0008075255,0.001018822,0.001778827],"category_scores_gemma":[0.005076274,0.0002785345,0.001205395,0.003572627,0.0003385133,0.001578278,0.001026736,0.0008894407,0.001212628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008769283,"about_ca_system_score_gemma":0.0007634251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005235873,"about_ca_topic_score_gemma":0.005965411,"domain_scores_codex":[0.9989495,0.000334998,0.0001171098,0.0002956029,0.0001757144,0.0001271878],"domain_scores_gemma":[0.9967513,0.002224457,0.000354861,0.0001517798,0.0003983177,0.000119425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006521008,0.00117816,0.2708546,0.001069178,0.0004176319,0.001251138,0.002231973,0.05559195,0.009372818,0.004119235,0.02636705,0.6268942],"study_design_scores_gemma":[0.00002558038,0.0001428633,0.0435639,0.0001166732,0.0001095247,0.0003308109,0.001596195,0.9358703,0.003428377,0.006440231,0.008321043,0.00005462256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6777253,0.002229534,0.2734613,0.004774959,0.000465989,0.001859365,0.02235529,0.003827224,0.01330098],"genre_scores_gemma":[0.820003,0.0007856828,0.1611773,0.0002777364,0.0004337173,0.0008671795,0.01303535,0.00007114673,0.003348871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006818783,"threshold_uncertainty_score":0.01041079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1501682910111418,"score_gpt":0.3782085963368849,"score_spread":0.2280403053257431,"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."}}