{"id":"W2889291357","doi":"10.1017/pan.2018.30","title":"Ideological Scaling of Social Media Users: A Dynamic Lexicon Approach","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université Laval","funders":"University of Cambridge; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Compute Canada; Université Laval","keywords":"Ideology; Social media; Voting behavior; Lexicon; Politics; Voting; Dimension (graph theory); Rhetoric; Sociology; Computer science; Political science; Linguistics; Artificial intelligence; Law; Mathematics; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003866584,0.0005807362,0.0008238272,0.008680145,0.001385953,0.004835264,0.001129242,0.0008682817,0.003152896],"category_scores_gemma":[0.03665362,0.0005149004,0.0009683371,0.006090068,0.001917576,0.005175007,0.003013638,0.001097053,0.0009227694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130956,"about_ca_system_score_gemma":0.00085068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002407346,"about_ca_topic_score_gemma":0.002708683,"domain_scores_codex":[0.9956117,0.002326533,0.0003033253,0.0009441947,0.0006386566,0.0001755778],"domain_scores_gemma":[0.9815302,0.01392238,0.001593763,0.001632288,0.001040803,0.0002805899],"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":[0.0005615852,0.000858164,0.1361683,0.0005909087,0.0005833551,0.0006411146,0.02042319,0.02346293,0.01008712,0.2924798,0.006342283,0.5078012],"study_design_scores_gemma":[0.00008528243,0.0001480172,0.07080494,0.0001591556,0.0001413834,0.0004275903,0.00760382,0.5573376,0.002633529,0.3472471,0.01326479,0.000146707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3650187,0.0004778806,0.6043807,0.001283666,0.0001089236,0.0006709094,0.003079311,0.0006060119,0.0243739],"genre_scores_gemma":[0.8863655,0.0001460762,0.1094967,0.00009466286,0.0001000836,0.0006667295,0.001689114,0.00009868506,0.001342519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008680145,"threshold_uncertainty_score":0.02044868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06638905123520183,"score_gpt":0.4087408830263073,"score_spread":0.3423518317911055,"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."}}