{"id":"W3199869264","doi":"10.1109/tcss.2021.3108810","title":"Latent Personality Traits Assessment From Social Network Activity Using Contextual Language Embedding","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Personality Traits and Psychology","field":"Psychology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Personality; Big Five personality traits; Word2vec; Context (archaeology); Natural language processing; Artificial intelligence; Representation (politics); Social media; Set (abstract data type); Feature (linguistics); Social network (sociolinguistics); Machine learning; Embedding; Psychology; World Wide Web; Social psychology; Linguistics","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":[0.0003487258,0.0004533287,0.0002439068,0.00143258,0.0001658595,0.0005584176,0.0001628539,0.0002157578,0.001002893],"category_scores_gemma":[0.001850625,0.00008935871,0.0003281022,0.0008433376,0.0001159004,0.0006238227,0.000423362,0.0003029341,0.0006205873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809137,"about_ca_system_score_gemma":0.0001523337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00132846,"about_ca_topic_score_gemma":0.00292186,"domain_scores_codex":[0.9996935,0.000102764,0.00002768101,0.00008063324,0.00005959203,0.00003587584],"domain_scores_gemma":[0.9989333,0.0004433109,0.0002290205,0.0001043727,0.0002220446,0.00006792411],"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":[0.0008037523,0.0006405963,0.3364521,0.0002794811,0.0002823766,0.000579037,0.0008817017,0.0228561,0.04165363,0.00155107,0.004032308,0.5899879],"study_design_scores_gemma":[0.00001500503,0.0003350427,0.2681373,0.0000390067,0.0001246625,0.0005341917,0.0008581367,0.7086209,0.01609471,0.002046336,0.003131593,0.00006318786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8968884,0.0002458783,0.09750763,0.0001318436,0.00004687342,0.00009491619,0.001696669,0.0006261812,0.002761687],"genre_scores_gemma":[0.9798082,0.00009919439,0.01808708,0.00001336415,0.0000214202,0.00004419496,0.0009890319,0.00001082314,0.0009266671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00143258,"threshold_uncertainty_score":0.003354967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07913171561137664,"score_gpt":0.3976574496186571,"score_spread":0.3185257340072805,"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."}}