{"id":"W4225003922","doi":"10.1145/3491102.3501940","title":"VibEmoji: Exploring User-authoring Multi-modal Emoticons in Social Communication","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems","topic":"Digital Communication and Language","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); The Scarborough Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Modal; Human–computer interaction; Multimedia; 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":[0.001172594,0.0006635294,0.0002644896,0.0004196341,0.0005089939,0.001470428,0.000643336,0.0007828966,0.002873934],"category_scores_gemma":[0.004536476,0.0002781251,0.0003748805,0.0002385315,0.0005748407,0.00153897,0.001359506,0.0005706841,0.0005484225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002267314,"about_ca_system_score_gemma":0.000251424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003942795,"about_ca_topic_score_gemma":0.001234939,"domain_scores_codex":[0.9993916,0.0003994406,0.00001736266,0.00008124731,0.00006481567,0.00004557952],"domain_scores_gemma":[0.9971526,0.00234104,0.0001218755,0.0001470369,0.0001107461,0.0001267196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002220022,0.001681795,0.03716192,0.004677628,0.0002312351,0.002052945,0.1364933,0.00936343,0.4061309,0.02172175,0.01404432,0.3642208],"study_design_scores_gemma":[0.0007256653,0.007152062,0.1473961,0.001525619,0.00086073,0.005957806,0.05905311,0.2655296,0.1244563,0.05102672,0.3355755,0.000740887],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8078535,0.0006605896,0.1726306,0.0004171805,0.00008897427,0.0004734685,0.000348469,0.002323783,0.01520344],"genre_scores_gemma":[0.8522012,0.0003859533,0.1351022,0.0002847478,0.00003332996,0.0006670851,0.0003743282,0.0002918409,0.01065935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002873934,"threshold_uncertainty_score":0.009614229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.276257002162567,"score_gpt":0.3604159060586858,"score_spread":0.08415890389611885,"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."}}