{"id":"W2344499866","doi":"10.3233/web-160333","title":"Predicting political conflicts from polarized social media","year":2016,"lang":"en","type":"article","venue":"Web Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Politics; Social media; Political science; Political economy; Social psychology; Sociology; Psychology; Law","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":[0.0002559942,0.000132128,0.0001948184,0.00009196259,0.0001397671,0.0001377417,0.0008270216,0.00008168365,0.0003955922],"category_scores_gemma":[0.0003272235,0.00009317232,0.000114031,0.000248857,0.00009803608,0.0003198897,0.0002660677,0.00009644884,0.0004936045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004505704,"about_ca_system_score_gemma":0.00006073936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007018844,"about_ca_topic_score_gemma":0.0000214411,"domain_scores_codex":[0.9983578,0.00007649596,0.0003289826,0.0004160767,0.0003905904,0.0004300269],"domain_scores_gemma":[0.9985764,0.0007698486,0.00009517824,0.0003117791,0.00008429174,0.000162481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001057749,0.00005571169,0.01898881,0.000002588627,0.00008056629,0.00001550889,0.002764687,6.30838e-7,0.0362686,0.8835447,0.0003304235,0.05793721],"study_design_scores_gemma":[0.002328168,0.0002330344,0.06403479,0.0006191492,0.0002011364,0.00002814219,0.002785432,0.2647696,0.4437988,0.1738983,0.04439107,0.002912297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08372799,0.0001129777,0.905475,0.005338696,0.001010967,0.00007165634,0.00002052872,0.0002596773,0.003982542],"genre_scores_gemma":[0.9923685,0.00001232548,0.006628274,0.0003468196,0.0004971601,0.000003864475,0.00000353019,0.000007455533,0.0001321267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9086404,"threshold_uncertainty_score":0.6344451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04654038883628945,"score_gpt":0.2870616059826523,"score_spread":0.2405212171463629,"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."}}