{"id":"W2885581957","doi":"10.1007/s10844-018-0519-2","title":"Predicting future personal life events on twitter via recurrent neural networks","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Mental Health via Writing","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Event (particle physics); Identification (biology); Phone; Variety (cybernetics); Social media; Task (project management); Personal life; Data science; Baseline (sea); World Wide Web; Personally identifiable information; Mobile phone; Internet privacy; Human–computer interaction; Artificial intelligence; Computer security; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401852,0.0001928631,0.0003083386,0.000314528,0.0001903133,0.00008079082,0.000245713,0.0001690913,0.0004069558],"category_scores_gemma":[0.00006491449,0.0001530517,0.0001488821,0.0002012387,0.00003008598,0.0006514685,0.00003249583,0.0006306247,0.000581024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002646193,"about_ca_system_score_gemma":0.00004420603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002638899,"about_ca_topic_score_gemma":0.000001219557,"domain_scores_codex":[0.9966969,0.0002444872,0.001888317,0.0001019276,0.000686336,0.0003820671],"domain_scores_gemma":[0.9970304,0.000113682,0.001848769,0.0001626582,0.0005400608,0.0003043835],"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":[0.006160814,0.0009375921,0.18739,0.001393529,0.00119004,0.00006035537,0.1667395,0.007294888,0.00002627594,0.002764896,0.1875612,0.438481],"study_design_scores_gemma":[0.004419472,0.009062635,0.0354639,0.003871365,0.0001416444,0.004477285,0.139148,0.5828184,0.0001372254,0.00002952613,0.2193203,0.001110174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116692,0.0007888859,0.03503068,0.0007337416,0.04289929,0.0007454738,0.00001297001,0.00005302548,0.008066745],"genre_scores_gemma":[0.9898676,0.00002151643,0.00004270673,0.001805095,0.008114802,0.00001308164,0.00001264603,0.00001477513,0.0001077348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5755236,"threshold_uncertainty_score":0.7468082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04665183238553716,"score_gpt":0.3480449519037792,"score_spread":0.301393119518242,"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."}}