{"id":"W2949542275","doi":"10.1145/3292500.3330774","title":"Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration","year":2019,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; John D. and Catherine T. MacArthur Foundation; National Science Foundation","keywords":"Leverage (statistics); Computer science; Newspaper; Social media; Conversation; Big data; Data science; Movement (music); Variable (mathematics); Artificial intelligence; Machine learning; Econometrics; Data mining; Sociology; Mathematics; Media studies; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002247313,0.0008896959,0.0004082964,0.003513535,0.0004096555,0.001934302,0.0006503075,0.000802425,0.001703453],"category_scores_gemma":[0.009371313,0.0003424709,0.0005380894,0.002610662,0.0005866099,0.002004238,0.00128184,0.0009777239,0.0008348536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005198828,"about_ca_system_score_gemma":0.0005392994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01318567,"about_ca_topic_score_gemma":0.02444486,"domain_scores_codex":[0.999116,0.0004609246,0.00004965889,0.0001585261,0.0001275215,0.00008730414],"domain_scores_gemma":[0.9943116,0.003603466,0.0008364829,0.0004518522,0.000580159,0.0002164014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003288254,0.0003699965,0.8459886,0.0002159285,0.0004503198,0.0002672122,0.0007982575,0.0418519,0.001465487,0.003307351,0.003545745,0.1014104],"study_design_scores_gemma":[0.00003864505,0.000257071,0.3550407,0.0002659837,0.0003111773,0.0001998197,0.003309614,0.6155196,0.002054119,0.01449215,0.008408942,0.0001022379],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9201708,0.000703402,0.06501266,0.00180766,0.0002438675,0.000123658,0.004877079,0.0003691534,0.006691869],"genre_scores_gemma":[0.9838461,0.0002114363,0.01274479,0.00008265083,0.0001211105,0.00004483289,0.002194334,0.00002103754,0.0007337323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01318567,"threshold_uncertainty_score":0.02621788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740439724584318,"score_gpt":0.2589286778079679,"score_spread":0.2315242805621247,"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."}}