{"id":"W3108885890","doi":"","title":"An Empirical Text Mining Analysis of Fort McMurray Wildfire Disaster Twitter Communication using Topic Model","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Crisis communication; Emergency management; Computer science; Computer security; Geography; Natural language processing; Political science; Public relations","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00280667,0.00009600582,0.0002635171,0.0002981796,0.0004340381,0.00004944653,0.0003781315,0.00007019563,0.00007410964],"category_scores_gemma":[0.00008468116,0.00006708124,0.0002607628,0.0007228153,0.0001463844,0.0003801663,0.00003160836,0.0002810738,0.000001569928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000527773,"about_ca_system_score_gemma":0.00103026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002915229,"about_ca_topic_score_gemma":0.003640656,"domain_scores_codex":[0.9977493,0.0005480047,0.0003624968,0.0001635008,0.0004124821,0.000764268],"domain_scores_gemma":[0.9990792,0.0002274191,0.0002556767,0.0002183532,0.0001277323,0.00009162854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008356017,0.0003203496,0.567542,0.000003480861,0.003119749,0.000001152334,0.02797446,0.01722264,0.002233119,0.06101459,0.00005683737,0.3204281],"study_design_scores_gemma":[0.0009953981,0.0002112531,0.050126,0.00007427188,0.002979319,0.00001747211,0.02440505,0.4456474,0.00006406212,0.4737147,0.001164829,0.0006002577],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6841937,0.0001794337,0.3140132,0.001108174,0.00002018436,0.00002892581,7.774917e-7,0.00000811963,0.0004475486],"genre_scores_gemma":[0.9922016,0.000263583,0.006530678,0.0001019031,0.000117456,0.00000133336,0.000003070187,0.000008000697,0.0007723524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5174159,"threshold_uncertainty_score":0.3338314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0687438818837136,"score_gpt":0.4213689164384264,"score_spread":0.3526250345547128,"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."}}