{"id":"W55067354","doi":"","title":"INFORMATIONAL SUPPORT OR EMOTIONAL SUPPORT: PRELIMINARY STUDY OF AN AUTOMATED APPROACH TO ANALYZE ONLINE SUPPORT COMMUNITY CONTENTS","year":2010,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Emotional support; Content analysis; Support vector machine; Qualitative analysis; Online community; Machine learning; Data mining; Qualitative research; World Wide Web; Social support; Psychology","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":[0.001154685,0.0002676695,0.0003885433,0.0009860059,0.0002432764,0.0006435596,0.001903291,0.0001228332,0.0004392016],"category_scores_gemma":[0.000149806,0.0002267671,0.00009523812,0.0005346003,0.00004838557,0.004251486,0.0003420862,0.0004414023,0.0002455603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000731719,"about_ca_system_score_gemma":0.0002869776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002521058,"about_ca_topic_score_gemma":0.00005060196,"domain_scores_codex":[0.9960965,0.0001932081,0.001609392,0.0002337602,0.001613989,0.0002531669],"domain_scores_gemma":[0.9963139,0.0001033118,0.0009171841,0.0007275325,0.001706032,0.000232078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002736418,0.02948018,0.03769897,0.0006029775,0.003449259,0.00003614736,0.1797584,0.1072229,0.001650349,0.5423483,0.05878955,0.03622653],"study_design_scores_gemma":[0.001830566,0.002066274,0.03627528,0.00004804906,0.00002168374,0.00008245133,0.01404317,0.9416472,0.00009044899,0.00002578094,0.003530282,0.0003387626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9263216,3.289716e-7,0.03163518,0.0003362214,0.002448405,0.001193522,0.0004182831,0.0004336493,0.03721279],"genre_scores_gemma":[0.9933085,7.630287e-7,0.002706396,0.0004377578,0.00008545069,0.00008076168,0.002973205,0.00000777504,0.0003993661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8344244,"threshold_uncertainty_score":0.9247295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0816712906517177,"score_gpt":0.3504108192172091,"score_spread":0.2687395285654914,"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."}}