{"id":"W3087926002","doi":"10.1145/3415959.3416000","title":"Predicting Twitter Engagement With Deep Language Models","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Leverage (statistics); Language model; Benchmarking; Social media; World Wide Web; Task (project management); User engagement; Code (set theory); Data science; Deep learning; Information retrieval; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000135486,0.0000803267,0.00009029313,0.00002129335,0.00005109312,0.0001146873,0.0003812271,0.00001972473,0.00002178155],"category_scores_gemma":[0.000002956547,0.00005380312,0.00002184708,0.0001214502,0.000005534626,0.0003434856,0.0001886839,0.00009178663,0.00001286142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001014784,"about_ca_system_score_gemma":0.00001013274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007510866,"about_ca_topic_score_gemma":0.000009791253,"domain_scores_codex":[0.9993187,0.00003925185,0.0001142923,0.0002316286,0.0001548141,0.0001412843],"domain_scores_gemma":[0.9996112,0.00001868901,0.00003534932,0.0002402897,0.00001906148,0.00007543286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003642996,0.0002702103,0.02634105,0.0003675957,0.0003772071,0.0005126839,0.2634571,0.003065781,0.004087029,0.397485,0.0591463,0.2448536],"study_design_scores_gemma":[0.0002575472,0.0002151769,0.0001394059,0.00002178489,0.000004102517,0.00001291322,0.0009485789,0.9872332,0.006426756,0.0007754061,0.003760992,0.0002041253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006891109,0.00003666448,0.9477895,0.002987206,0.00003202483,0.0001432409,1.711571e-7,0.0006442472,0.04147585],"genre_scores_gemma":[0.8566945,0.000001515384,0.1403541,0.002746648,0.00005459095,0.0000187992,4.24892e-7,0.000005963142,0.0001234095],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9841675,"threshold_uncertainty_score":0.2194028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04790758784266261,"score_gpt":0.2522579112034721,"score_spread":0.2043503233608095,"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."}}