{"id":"W4409177773","doi":"10.1007/978-981-96-3236-7_51","title":"Harnessing Twitter Sentiments for Short-Term Stock Predictions in the Digital Age","year":2025,"lang":"en","type":"book-chapter","venue":"Applied economics and policy studies","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Term (time); Stock (firearms); Computer science; Business; Financial economics; Economics; History; Physics; Astronomy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00146535,0.000344964,0.0006904865,0.0005269686,0.0003724065,0.0006143807,0.0004785907,0.0001601268,0.000007576526],"category_scores_gemma":[0.0004769097,0.0002482971,0.0001699043,0.00007776886,0.0002694886,0.00008346236,0.0004732632,0.000216992,0.000009301836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130529,"about_ca_system_score_gemma":0.00008660384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006273216,"about_ca_topic_score_gemma":0.00006653562,"domain_scores_codex":[0.9980839,0.00002320788,0.0007860342,0.0006488004,0.0001622954,0.0002957129],"domain_scores_gemma":[0.9954445,0.00367855,0.0002696204,0.000505385,0.00005591048,0.00004601681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000105509,0.00003381602,0.001207616,0.00007438775,0.000752026,0.000003760523,0.005608178,0.0001131464,0.000001753433,0.3350716,0.05086183,0.6061664],"study_design_scores_gemma":[0.0004313403,0.00003834689,0.003262121,0.00009049011,0.0001000576,0.000007169183,0.0008854532,0.0002709785,0.000002370777,0.5401036,0.4544213,0.0003868258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009391986,0.0005040393,0.0006549377,0.001540384,0.0005152659,0.001327231,0.0004336312,0.00002705332,0.9856055],"genre_scores_gemma":[0.3219753,0.00196487,0.002933266,0.002859808,0.002089255,0.0008233757,0.00009348656,0.0001202212,0.6671404],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6057796,"threshold_uncertainty_score":0.9999969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1919603718103821,"score_gpt":0.4170126909578481,"score_spread":0.225052319147466,"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."}}