{"id":"W2015209454","doi":"10.1016/j.socnet.2011.05.006","title":"Geography of Twitter networks","year":2011,"lang":"en","type":"article","venue":"Social Networks","topic":"Social Media and Politics","field":"Social Sciences","cited_by":619,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; University of Toronto","funders":"","keywords":"Metropolitan area; Interpersonal ties; Strong ties; Sample (material); Geographical distance; Social media; Geography; Economic geography; Advertising; Sociology; Business; Computer science; World Wide Web; Social science; Demography; Population","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":[0.00035983,0.0001585866,0.0001746497,0.003743684,0.001391683,0.005114093,0.0002976325,0.0006106325,0.0176824],"category_scores_gemma":[0.00427905,0.0001959684,0.0001993522,0.004199651,0.001993757,0.004174986,0.001723084,0.0004985682,0.00180189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002338878,"about_ca_system_score_gemma":0.0009983059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237881,"about_ca_topic_score_gemma":0.01460287,"domain_scores_codex":[0.9993984,0.0002945271,0.00002319843,0.00009367879,0.0001083861,0.00008179705],"domain_scores_gemma":[0.9983285,0.0006633714,0.0004007356,0.0001279642,0.0002337769,0.0002457055],"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":[0.00003908998,0.00001018868,0.01021717,0.00007257231,0.00001201049,0.0001356939,0.002343426,0.001863333,0.0003777512,0.9621553,0.01020748,0.01256589],"study_design_scores_gemma":[0.00002661986,0.00003509912,0.05559419,0.0001823516,0.00002621923,0.0006179114,0.01302861,0.005722824,0.0005017216,0.5534058,0.3708181,0.00004061088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2820479,0.004522357,0.02130073,0.01834748,0.0002120548,0.0001186712,0.006773383,0.0002394302,0.666438],"genre_scores_gemma":[0.9697043,0.002365001,0.001680939,0.0001656544,0.0001676377,0.00006816743,0.001057024,0.00005282577,0.02473845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0176824,"threshold_uncertainty_score":0.05915356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04618489578886176,"score_gpt":0.2976374578870803,"score_spread":0.2514525620982185,"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."}}