{"id":"W3081613086","doi":"10.1177/0022242920937703","title":"Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue","year":2020,"lang":"en","type":"article","venue":"Journal of Marketing","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Box office; Revenue; Advertising; Plot (graphics); Studio; Metric (unit); Word of mouth; Event (particle physics); Measure (data warehouse); Film industry; Econometrics; Computer science; Marketing; Operations research; Engineering; Mathematics; Business; Data mining; Statistics; Movie theater; Art; Visual arts","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02450178,0.0001598117,0.0005385918,0.00007552983,0.0003147857,0.0001077589,0.0004935124,0.00009672672,0.00003619203],"category_scores_gemma":[0.04377993,0.0001106452,0.0003092125,0.0004899565,0.00007378855,0.000167178,0.00005812803,0.000446162,0.000008509078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228727,"about_ca_system_score_gemma":0.0001727283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001006514,"about_ca_topic_score_gemma":0.00003166895,"domain_scores_codex":[0.995093,0.002758913,0.0007556136,0.0001772461,0.0008952482,0.0003199966],"domain_scores_gemma":[0.9962257,0.002432318,0.0006942375,0.0001483781,0.0002406497,0.0002586838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01836395,0.000167691,0.02504297,0.001791795,0.0004191285,0.0002136443,0.08852456,0.02055432,0.007160405,0.0003630927,0.03201186,0.8053866],"study_design_scores_gemma":[0.009124803,0.007659804,0.02161697,0.06523721,0.002228729,0.0001077812,0.0652687,0.008676301,0.00203745,0.0006931406,0.8132911,0.004058059],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974277,0.0008966281,0.0001033964,0.003605684,0.0007162601,0.0003400957,0.000003506407,0.00001222858,0.02004515],"genre_scores_gemma":[0.9968925,0.0003127742,0.0004137432,0.000547162,0.001689092,0.000002123231,2.071811e-7,0.00001995192,0.0001224297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8013285,"threshold_uncertainty_score":0.9642747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05825817435868177,"score_gpt":0.3223259619082517,"score_spread":0.2640677875495699,"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."}}