{"id":"W3007795301","doi":"10.3390/data5010020","title":"VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data","year":2020,"lang":"en","type":"article","venue":"Data","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Microblogging; Computer science; Social media; Analytics; Visual analytics; Data science; Aggregate (composite); Data analysis; Social media analytics; Visualization; World Wide Web; Data mining","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":[0.004011046,0.002077945,0.0009721521,0.004847263,0.0008310953,0.004365507,0.002744974,0.00141437,0.02197877],"category_scores_gemma":[0.0175755,0.001020151,0.001233638,0.002213654,0.0007938902,0.005581309,0.004413041,0.002586165,0.006385473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008446227,"about_ca_system_score_gemma":0.001446512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003903088,"about_ca_topic_score_gemma":0.005622251,"domain_scores_codex":[0.9987103,0.0003925831,0.0001291648,0.0002790854,0.0003930521,0.00009589327],"domain_scores_gemma":[0.992566,0.004298333,0.000475336,0.0009293678,0.001248093,0.000482879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002591031,0.0004360311,0.008677466,0.003021553,0.0005559474,0.00148488,0.007022698,0.02302003,0.03727397,0.03388106,0.4454329,0.4366025],"study_design_scores_gemma":[0.0004816443,0.0003659591,0.006458031,0.0007631804,0.0001762328,0.0009791233,0.001498952,0.4577804,0.03346318,0.09468684,0.4027103,0.0006362033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007483372,0.000559808,0.6823362,0.001714644,0.0004655069,0.0007612288,0.02054713,0.2787183,0.007413939],"genre_scores_gemma":[0.1066312,0.0009173314,0.8425397,0.001219971,0.0003044936,0.00202474,0.02238359,0.01641451,0.007564608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02197877,"threshold_uncertainty_score":0.07352632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.163996349514397,"score_gpt":0.3829647914643339,"score_spread":0.2189684419499369,"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."}}