{"id":"W4361018126","doi":"10.2139/ssrn.4401866","title":"Integrating Social Media Data: Venues, Groups and Activities","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Social media; Business; Computer science; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009916697,0.0007677287,0.0007515072,0.01399622,0.0006887374,0.002684943,0.0008843965,0.0008782783,0.00703261],"category_scores_gemma":[0.006726413,0.0003224617,0.0007621551,0.01453353,0.0002423645,0.003965955,0.002022132,0.0009222961,0.004700186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006152537,"about_ca_system_score_gemma":0.0007499019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01015841,"about_ca_topic_score_gemma":0.02326854,"domain_scores_codex":[0.998187,0.0004075079,0.0002078833,0.0004127021,0.0006504398,0.0001345202],"domain_scores_gemma":[0.9958485,0.001751835,0.0004449763,0.0008648727,0.0007333314,0.0003565495],"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":[0.0006300922,0.0006700123,0.2928873,0.002489077,0.0008165834,0.0005328138,0.003752267,0.0112524,0.01779492,0.01786864,0.06653347,0.5847723],"study_design_scores_gemma":[0.000048341,0.0002296271,0.3622289,0.0007454065,0.0008282373,0.001315254,0.01276802,0.141806,0.01966662,0.04651731,0.4136238,0.0002224606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2812951,0.002984813,0.1731752,0.001785296,0.0009861746,0.0008782015,0.4827807,0.005382628,0.05073192],"genre_scores_gemma":[0.6277522,0.001758741,0.1430014,0.0001922694,0.0006830144,0.0008740925,0.2105108,0.0004898814,0.01473747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01399622,"threshold_uncertainty_score":0.02352643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0209063892034339,"score_gpt":0.2837472410326227,"score_spread":0.2628408518291888,"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."}}