{"id":"W3125521553","doi":"10.2308/accr-51865","title":"Can Twitter Help Predict Firm-Level Earnings and Stock Returns?","year":2017,"lang":"en","type":"article","venue":"The Accounting Review","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":607,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Earnings; Stock (firearms); Business; Exploit; Dissemination; Social media; Stock market; Economics; Accounting; Computer science; World Wide Web","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":[0.001299835,0.0002101432,0.0002338882,0.001243699,0.0001339271,0.001288463,0.0002002575,0.0004196857,0.003535278],"category_scores_gemma":[0.009899213,0.0001083147,0.0003006181,0.001420916,0.0001444154,0.001110455,0.0003311972,0.0004175241,0.001001167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002616679,"about_ca_system_score_gemma":0.0002268573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004907385,"about_ca_topic_score_gemma":0.007087367,"domain_scores_codex":[0.9996414,0.0001477032,0.00003803093,0.00004702794,0.00007767571,0.00004812142],"domain_scores_gemma":[0.9936062,0.003224066,0.001942319,0.0001715737,0.0007624757,0.0002933417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001859919,0.0000662703,0.9372164,0.0002613972,0.0002438472,0.00009982991,0.0003673375,0.0008587541,0.0004648293,0.0009751452,0.004871069,0.05438907],"study_design_scores_gemma":[0.00001625054,0.0001157757,0.9723973,0.0002977519,0.0002907364,0.0001162185,0.001479414,0.009288342,0.0006423265,0.001776055,0.01355205,0.00002791544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9611017,0.009890733,0.001387539,0.006313854,0.0002673788,0.00003260424,0.004137551,0.00003561543,0.016833],"genre_scores_gemma":[0.9941372,0.00286817,0.0003551575,0.0001906061,0.0002694459,0.00001072998,0.0008223176,0.000004448547,0.001341935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004907385,"threshold_uncertainty_score":0.01182663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07845876055274723,"score_gpt":0.2607725343957056,"score_spread":0.1823137738429584,"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."}}