{"id":"W2518949142","doi":"10.1145/2938503.2938508","title":"The Twitter Bullishness Index","year":2016,"lang":"en","type":"article","venue":"","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"tf–idf; Index (typography); Lexicon; Analytics; Pearson product-moment correlation coefficient; Computer science; Natural language processing; Social media; Stock market index; Artificial intelligence; Social media analytics; Stock market; Econometrics; Statistics; Term (time); Data science; History; Mathematics; World Wide Web; Context (archaeology)","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":[0.0006828484,0.0002499378,0.0002315508,0.00256063,0.0003377053,0.0009666005,0.0002255548,0.000312418,0.004805525],"category_scores_gemma":[0.006489696,0.00006371536,0.000204267,0.002011704,0.0002204552,0.001240479,0.0008375216,0.0003632873,0.00151468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003786668,"about_ca_system_score_gemma":0.0001940355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004210451,"about_ca_topic_score_gemma":0.004637589,"domain_scores_codex":[0.9992846,0.0001286848,0.0001017102,0.00008184103,0.0003068613,0.00009630263],"domain_scores_gemma":[0.9938006,0.001479266,0.002710208,0.0003005227,0.001150265,0.0005591794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002564717,0.00008331572,0.922991,0.000260881,0.0001780384,0.0002402965,0.001552984,0.0008782644,0.003966053,0.001531843,0.01316042,0.05490025],"study_design_scores_gemma":[0.000006875409,0.0001421507,0.9752532,0.00005966567,0.0000825528,0.0003667427,0.001996668,0.003521739,0.001644681,0.0007318654,0.01615741,0.00003659571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602087,0.0009249549,0.001694722,0.0007807766,0.00007950333,0.0001003144,0.01332647,0.0002433542,0.02264112],"genre_scores_gemma":[0.9917384,0.0003079379,0.0005757146,0.0001215651,0.00009699423,0.00004493571,0.004267112,0.00001507219,0.002832355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004805525,"threshold_uncertainty_score":0.01607609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953710615003552,"score_gpt":0.2032965721038418,"score_spread":0.1737594659538063,"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."}}