{"id":"W4408618891","doi":"10.1016/j.trpro.2025.03.056","title":"Social Media as a Market Prophecy: Leveraging ML Algorithms for Predicting Market Trends and Demand","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Anti-Doping Agency","funders":"","keywords":"Social media; Supply and demand; Computer science; On demand; Economics; Algorithm; Business; Microeconomics; World Wide Web; Multimedia","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":[0.002535384,0.001178541,0.00081277,0.003050624,0.0004122556,0.002426647,0.001205436,0.001022913,0.002639099],"category_scores_gemma":[0.01357516,0.0004515357,0.0007673004,0.001889788,0.0008376082,0.005242573,0.001455364,0.001861926,0.001251184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008609039,"about_ca_system_score_gemma":0.0009672462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005375992,"about_ca_topic_score_gemma":0.007797985,"domain_scores_codex":[0.999126,0.0003693751,0.00004638291,0.0001764476,0.0002131527,0.00006859802],"domain_scores_gemma":[0.9936721,0.00474595,0.000496378,0.0005167331,0.0004567206,0.0001121058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003390018,0.0004451176,0.01970656,0.0001986829,0.0002276226,0.0002695154,0.0003615037,0.4671517,0.002572806,0.03340729,0.00837888,0.4669414],"study_design_scores_gemma":[0.000004316008,0.00001926971,0.0002739602,0.000005950646,0.000006435993,0.000009755965,0.0000236267,0.9901388,0.0003440822,0.008504656,0.000664469,0.000004655369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09038355,0.001270679,0.8928248,0.003543645,0.0001887487,0.0002037828,0.0009248127,0.003202919,0.00745713],"genre_scores_gemma":[0.8537484,0.0007901055,0.1389767,0.0005571535,0.0004591857,0.00015234,0.001325631,0.0002434732,0.003746921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005375992,"threshold_uncertainty_score":0.0134086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06853170726011798,"score_gpt":0.4104725822551354,"score_spread":0.3419408749950174,"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."}}