{"id":"W4382699635","doi":"10.1177/10949968231172153","title":"Should We Feed the Trolls? Using Marketer-Generated Content to Explain Average Toxicity and Product Usage","year":2023,"lang":"en","type":"article","venue":"Journal of Interactive Marketing","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; HEC Montréal","funders":"Institut de Valorisation des Données","keywords":"Social media; Product (mathematics); Set (abstract data type); Quality (philosophy); Computer science; Toxicity; Field (mathematics); Advertising; User-generated content; New product development; Marketing; Business; World Wide Web; Mathematics; Chemistry","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":[0.002967189,0.0006602873,0.0005034783,0.002235393,0.0005991671,0.002616176,0.0005568574,0.001616315,0.0036479],"category_scores_gemma":[0.02559284,0.000285276,0.0006231118,0.001890829,0.001048525,0.00517457,0.0006940736,0.0012155,0.001370113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111925,"about_ca_system_score_gemma":0.0004544012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007410046,"about_ca_topic_score_gemma":0.006587697,"domain_scores_codex":[0.998904,0.0004825606,0.00007477905,0.0001636137,0.0002731972,0.0001018256],"domain_scores_gemma":[0.9842009,0.009122813,0.003326818,0.00105956,0.001904849,0.0003850886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009154066,0.0006173052,0.8148546,0.00056592,0.0003894073,0.0005470358,0.003860544,0.005871867,0.00421524,0.007820988,0.006782454,0.1535591],"study_design_scores_gemma":[0.00008987649,0.001198705,0.7620631,0.0005795571,0.0004657898,0.0008577004,0.00662703,0.1511584,0.006321702,0.03276492,0.03766448,0.0002086767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523087,0.001412671,0.02131148,0.005515172,0.0001901368,0.0002255689,0.002648219,0.0005208289,0.01586724],"genre_scores_gemma":[0.9864476,0.0004895665,0.008568891,0.0006116682,0.0001184186,0.00007641642,0.001106159,0.00008291381,0.002498409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007410046,"threshold_uncertainty_score":0.01569223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1068520493690972,"score_gpt":0.347986886236314,"score_spread":0.2411348368672168,"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."}}