{"id":"W4294571684","doi":"10.1007/s13278-022-00946-0","title":"Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study","year":2022,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Pandemic; Sentiment analysis; Social media; Perception; Coronavirus disease 2019 (COVID-19); Arabic; Vaccination; Computer science; Data science; Internet privacy; Psychology; Medicine; Artificial intelligence; World Wide Web; Virology; Linguistics; Disease","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":[0.0008393328,0.0002777379,0.000163379,0.001094758,0.000388859,0.0006589693,0.0003390851,0.0005651808,0.0009156922],"category_scores_gemma":[0.003262456,0.0001000989,0.0002131465,0.0006555382,0.0001930784,0.000764086,0.0003793925,0.0003553609,0.0002169119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006489411,"about_ca_system_score_gemma":0.0004687068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03542158,"about_ca_topic_score_gemma":0.05139283,"domain_scores_codex":[0.9996623,0.0001343292,0.00002749428,0.0000662364,0.00005864774,0.00005099361],"domain_scores_gemma":[0.9978024,0.001472098,0.0002863771,0.00009909611,0.0002239905,0.0001160026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004604723,0.0006745151,0.9037347,0.0001944341,0.0001208806,0.002306046,0.009279618,0.004149089,0.01031575,0.001361926,0.003915465,0.0634871],"study_design_scores_gemma":[0.00003358258,0.0004054155,0.761977,0.0001114303,0.000196244,0.001266144,0.03079373,0.1797862,0.01074963,0.002130043,0.01249066,0.00005994953],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963469,0.00007809608,0.001723478,0.0002585027,0.000004845977,0.00002325969,0.0005437371,0.00003083825,0.0009903718],"genre_scores_gemma":[0.9953896,0.00005516936,0.003326638,0.00003064709,0.000004719674,0.000009923293,0.0006017823,0.000006022175,0.0005753528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03542158,"threshold_uncertainty_score":0.07043082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08300221817925063,"score_gpt":0.3338486034082799,"score_spread":0.2508463852290293,"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."}}