{"id":"W2608294675","doi":"10.1111/coin.12117","title":"Finding Diachronic Like‐Minded Users","year":2017,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of New Brunswick","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Timestamp; Friendship; Topic model; Social media; Similarity (geometry); Context (archaeology); Data science; Interpersonal ties; Information retrieval; World Wide Web; Artificial intelligence; Sociology; Geography","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":[0.00125973,0.0005945647,0.0007620585,0.004394814,0.001116348,0.001326428,0.001003119,0.001222239,0.001098168],"category_scores_gemma":[0.007498988,0.0004679557,0.0007657128,0.002755943,0.0007496707,0.003354719,0.0014828,0.0007251084,0.0004631123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006244637,"about_ca_system_score_gemma":0.000490371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004571664,"about_ca_topic_score_gemma":0.007052607,"domain_scores_codex":[0.998909,0.0003046248,0.00005525112,0.0004322434,0.0001789436,0.0001199436],"domain_scores_gemma":[0.9957045,0.002104804,0.0009377065,0.0004629704,0.0005099855,0.0002799559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001252366,0.0008690453,0.3198668,0.0009532488,0.0006432898,0.001790836,0.007864791,0.07243776,0.04339228,0.07890186,0.008849476,0.4631782],"study_design_scores_gemma":[0.00003801196,0.0001773774,0.04889298,0.00008700991,0.0001434461,0.001411405,0.002940491,0.8694904,0.008205867,0.0604084,0.008094731,0.0001098746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5171467,0.001238547,0.4758576,0.0004998159,0.00005138782,0.0002017981,0.0008164395,0.0003716881,0.003815914],"genre_scores_gemma":[0.9320874,0.0003837338,0.06506326,0.00006399296,0.00006314208,0.00007965815,0.0006620752,0.00003115468,0.001565669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004571664,"threshold_uncertainty_score":0.009090126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04944606780090331,"score_gpt":0.352443335402613,"score_spread":0.3029972676017097,"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."}}