{"id":"W2157940332","doi":"10.1145/1718487.1718525","title":"Early online identification of attention gathering items in social media","year":2010,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Social media; Computer science; Identification (biology); Set (abstract data type); Task (project management); World Wide Web; Internet privacy; Information retrieval; Data 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":[0.0008005328,0.001017458,0.0006978136,0.00431966,0.000819868,0.00141352,0.0006289872,0.001154069,0.002903325],"category_scores_gemma":[0.004763862,0.0003660037,0.0005226147,0.003744118,0.0005608425,0.002733485,0.00134129,0.000788533,0.001653632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005525348,"about_ca_system_score_gemma":0.0002624792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002439253,"about_ca_topic_score_gemma":0.0039149,"domain_scores_codex":[0.9992835,0.0001634668,0.00004384734,0.0002135255,0.0001972827,0.00009839803],"domain_scores_gemma":[0.9971491,0.001554167,0.0003614519,0.0002798158,0.0005408368,0.0001146099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008391678,0.0004424994,0.09437234,0.001612622,0.0002792726,0.001346578,0.003774414,0.005572275,0.07008881,0.01511926,0.01108284,0.79547],"study_design_scores_gemma":[0.0000581204,0.0007839868,0.4727798,0.0004786126,0.0005460873,0.00435532,0.004350266,0.3400143,0.07248274,0.03649554,0.06741583,0.0002394077],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.540018,0.008526416,0.4067025,0.0009915697,0.0005721567,0.000828342,0.003379887,0.001288304,0.03769284],"genre_scores_gemma":[0.9163935,0.002301218,0.06732877,0.0001834146,0.0004471768,0.0003468877,0.001494991,0.00008376186,0.01142048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00431966,"threshold_uncertainty_score":0.009712577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479545160627812,"score_gpt":0.2799124049652795,"score_spread":0.2651169533590014,"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."}}