{"id":"W4360847996","doi":"10.3390/bs13040282","title":"Dynamic Characteristics and Evolution Analysis of Information Dissemination Theme of Social Networks under Emergencies","year":2023,"lang":"en","type":"article","venue":"Behavioral Sciences","topic":"Public Relations and Crisis Communication","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Latent Dirichlet allocation; Theme (computing); Topic model; Computer science; Social media; Coding (social sciences); Data science; Operations research; Artificial intelligence; Sociology; Social science; World Wide Web; Engineering","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":[0.001034564,0.00004216964,0.0001131292,0.0003982804,0.0005411396,0.00004930963,0.0001870066,0.00006101004,0.00004349957],"category_scores_gemma":[0.00005392898,0.00003859107,0.00004973724,0.003234394,0.0004905946,0.0007095898,0.00004723015,0.00003704291,0.000001154248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004294085,"about_ca_system_score_gemma":0.00005512078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008099999,"about_ca_topic_score_gemma":0.0006773713,"domain_scores_codex":[0.9991283,0.00008541773,0.0002606718,0.00006945145,0.0003381052,0.000118009],"domain_scores_gemma":[0.9994258,0.00005743185,0.0002600861,0.00007336552,0.0001601193,0.00002324517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008521308,0.000141146,0.4825814,0.00001235958,0.00007454833,5.943433e-8,0.04228533,0.001534943,0.001000146,0.383497,0.0003230027,0.08854152],"study_design_scores_gemma":[0.00001982551,0.00001717855,0.9482427,0.000003905488,0.0001073842,2.050332e-8,0.01721138,0.03361578,0.000002843153,0.0006137511,0.0001204605,0.00004474029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955824,0.00002918011,0.00260919,0.000532163,0.00006815617,0.00008055596,0.00001745219,0.00002614209,0.001054773],"genre_scores_gemma":[0.9995672,0.00015627,0.0001053842,0.000002776061,0.000006192982,0.000006925222,0.00008174016,0.000001064525,0.00007247351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4656613,"threshold_uncertainty_score":0.4162063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566818300374154,"score_gpt":0.3812825038666285,"score_spread":0.345614320862887,"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."}}