{"id":"W2579586109","doi":"10.1109/wi.2016.0020","title":"Discovering Credible Twitter Users in Stock Market Domain","year":2016,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Credibility; Social media; Stock (firearms); Stock market; Computer science; Business; Econometrics; World Wide Web; Economics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009074686,0.0001800989,0.0003096342,0.0004968782,0.00006754971,0.0001752015,0.0008608294,0.00008255445,0.009187412],"category_scores_gemma":[0.007997457,0.00009352304,0.0001123986,0.0009458432,0.0001133239,0.0005647428,0.0003800294,0.0001078203,0.0003360849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126432,"about_ca_system_score_gemma":0.00005933205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006888058,"about_ca_topic_score_gemma":0.0001808066,"domain_scores_codex":[0.9963755,0.0006569549,0.0006722628,0.0006560085,0.001144753,0.0004945193],"domain_scores_gemma":[0.9925476,0.006248325,0.0001490733,0.0008368782,0.00007925821,0.0001388334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002521583,0.00004588248,0.6880982,0.000004275848,0.00001219052,0.00003422083,0.0004615063,0.00001840069,0.00477061,0.0009513753,0.1303547,0.1749965],"study_design_scores_gemma":[0.001780318,0.0001014114,0.8019554,0.0001124679,0.000004934614,0.00002972235,0.0008520757,0.0008889325,0.001597435,0.1213486,0.07080834,0.0005203608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7027001,0.00001019651,0.1241834,0.001518693,0.0005051118,0.000169717,0.000003822367,0.00005521658,0.1708537],"genre_scores_gemma":[0.8244279,0.000003318735,0.08538197,0.0003042142,0.000116919,0.0000299335,1.665406e-7,0.00002677686,0.08970882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1744761,"threshold_uncertainty_score":0.9917184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1343801012234399,"score_gpt":0.4020701307179368,"score_spread":0.2676900294944968,"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."}}