{"id":"W2949453867","doi":"10.48550/arxiv.1311.1194","title":"Identifying Purpose Behind Electoral Tweets","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Automatic summarization; Computer science; Popularity; Task (project management); Class (philosophy); Baseline (sea); Key (lock); Event (particle physics); Artificial intelligence; Natural language processing; Information retrieval; Data science; Computer security; Political science","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":[0.000853704,0.0003542244,0.0003272678,0.002874978,0.0008371948,0.001210463,0.0002453145,0.0004736082,0.001417323],"category_scores_gemma":[0.00462569,0.000176963,0.0003564304,0.001525223,0.0002141393,0.001191081,0.0007982103,0.0004697908,0.001159785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003713966,"about_ca_system_score_gemma":0.000316155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002477207,"about_ca_topic_score_gemma":0.005333657,"domain_scores_codex":[0.9992999,0.0001906958,0.00008227999,0.0001390538,0.0001666825,0.0001213563],"domain_scores_gemma":[0.9969569,0.001375766,0.0006223212,0.0002815361,0.000576912,0.0001865504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001056417,0.0002258406,0.6990201,0.000615871,0.0001577234,0.001117662,0.005632382,0.0017617,0.042556,0.004653791,0.04529639,0.1979061],"study_design_scores_gemma":[0.00004012296,0.0001507093,0.8464857,0.0001513489,0.0001203118,0.001048593,0.007012126,0.04986611,0.02496725,0.005583372,0.0645027,0.00007162603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953065,0.0006595485,0.01516442,0.001033775,0.0003145229,0.0001748998,0.01427818,0.0009040211,0.01440571],"genre_scores_gemma":[0.9728707,0.0002697065,0.009498212,0.000148941,0.0002176953,0.000100977,0.01320953,0.00007449361,0.003609723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002874978,"threshold_uncertainty_score":0.004925609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209284209360459,"score_gpt":0.2126565646009929,"score_spread":0.09172814366494703,"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."}}