{"id":"W2296291818","doi":"10.1609/icwsm.v9i1.14627","title":"Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Geolocation; Inference; Social media; Computer science; Data science; Ground truth; Standardization; Social network (sociolinguistics); Data mining; Social network analysis; Set (abstract data type); Information retrieval; Machine learning; Artificial intelligence; 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":[0.01089435,0.001749516,0.00201619,0.008286777,0.0008539379,0.00413105,0.003757777,0.002904243,0.00222805],"category_scores_gemma":[0.03806603,0.001646089,0.001605296,0.009399505,0.002373349,0.009882528,0.0024349,0.003080125,0.001705698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002484552,"about_ca_system_score_gemma":0.003243334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115624,"about_ca_topic_score_gemma":0.01118431,"domain_scores_codex":[0.996305,0.001561115,0.0003307999,0.0009638603,0.0007191388,0.0001201735],"domain_scores_gemma":[0.939946,0.05177012,0.001572587,0.001486972,0.004837036,0.0003873412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001389534,0.00009836147,0.0135261,0.01667346,0.0005723424,0.0001031144,0.0006557291,0.01087123,0.000440213,0.02156397,0.02849737,0.9068592],"study_design_scores_gemma":[0.00007168796,0.0003422606,0.02374977,0.04568068,0.001638467,0.0009500636,0.00454153,0.09672964,0.003070957,0.09575603,0.7269464,0.0005225263],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005286345,0.9398714,0.03001423,0.01773852,0.001069853,0.0001093106,0.0007961715,0.0002822389,0.004831931],"genre_scores_gemma":[0.05698447,0.9082213,0.02684644,0.002245038,0.003442414,0.0001732978,0.001031131,0.0001177641,0.0009380333],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01115624,"threshold_uncertainty_score":0.05761546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06830082228721585,"score_gpt":0.3757495941980974,"score_spread":0.3074487719108815,"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."}}