{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016752,0.00007006077,0.0002240807,0.0001117517,0.0002065453,0.00005426344,0.0001453432,0.00006985288,0.000122721],"category_scores_gemma":[0.002924557,0.00006299371,0.00009093459,0.0005368176,0.000380919,0.0002003094,0.00005479174,0.0001656911,2.970582e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008250459,"about_ca_system_score_gemma":0.0002418488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002377146,"about_ca_topic_score_gemma":0.0007742355,"domain_scores_codex":[0.9987925,0.0000875931,0.0003269167,0.0001857402,0.0005040144,0.0001032018],"domain_scores_gemma":[0.9980447,0.0003445645,0.000230819,0.00002920491,0.001320111,0.0000305855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001662489,0.001001081,0.1314303,0.002052195,0.0008343751,0.000001193543,0.08003945,0.00002647912,0.001796462,0.7188551,0.000937031,0.06286009],"study_design_scores_gemma":[0.003466439,0.0001858913,0.4927799,0.01712974,0.01025758,0.000009103082,0.1923952,0.1689911,0.001736669,0.08874679,0.02257529,0.001726249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421713,0.002278683,0.0004590735,0.04829055,0.0004721595,0.0002617173,0.00003823397,0.00001648875,0.006011837],"genre_scores_gemma":[0.9960029,0.003258414,0.00005323473,0.0003775592,0.000262023,0.00001051818,0.000009963002,0.000002593211,0.00002286845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6301083,"threshold_uncertainty_score":0.350118,"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."}}