{"id":"W2606797676","doi":"10.1007/978-3-319-57351-9_32","title":"Using Cognitive Computing to Get Insights on Personality Traits from Twitter Messages","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Personality Traits and Psychology","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Watson; Agreeableness; Computer science; Openness to experience; IBM; Big Five personality traits; Cognitive computing; Neuroticism; Extraversion and introversion; Personality; Conscientiousness; Artificial intelligence; Machine learning; Cognition; Psychology; Social psychology","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":[0.0005841751,0.0007372079,0.0003472321,0.002957184,0.0004305481,0.002064245,0.0003012521,0.0004791351,0.004698651],"category_scores_gemma":[0.00492281,0.000154146,0.0004316149,0.002829761,0.0001732551,0.00152474,0.0007089616,0.0006072865,0.002278308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003882056,"about_ca_system_score_gemma":0.0002255324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003119119,"about_ca_topic_score_gemma":0.00558152,"domain_scores_codex":[0.9996538,0.00008693328,0.00001894024,0.00007517481,0.000106573,0.00005853766],"domain_scores_gemma":[0.9974968,0.001749279,0.0002171206,0.0001260624,0.00027215,0.0001385843],"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":[0.000576399,0.0005878044,0.2880093,0.000505237,0.0003250318,0.0003156165,0.00274653,0.002482479,0.01970674,0.003339566,0.01601133,0.6653939],"study_design_scores_gemma":[0.00004996904,0.0004534354,0.7709171,0.0002536297,0.0003196294,0.0005704979,0.008495843,0.151317,0.01604545,0.02752902,0.02383735,0.0002111769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8623108,0.001813693,0.08650119,0.001509588,0.0004774476,0.000371988,0.009739762,0.001913119,0.03536234],"genre_scores_gemma":[0.9201354,0.0008920936,0.06698782,0.0003398592,0.0003584943,0.000274818,0.003646178,0.0001135847,0.007251712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004698651,"threshold_uncertainty_score":0.01571858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092594192314672,"score_gpt":0.3776252240613289,"score_spread":0.2683658048298617,"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."}}