{"id":"W1814109117","doi":"10.1609/icwsm.v6i1.14267","title":"The Emergence of Conventions in Online Social Networks","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation","keywords":"Convention; Raising (metalworking); Process (computing); Social media; Social network (sociolinguistics); Early adopter; World Wide Web; Sociology; Internet privacy; Computer science; Political science; Social science; Engineering","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":[0.004032193,0.0003775244,0.0005667688,0.00325977,0.003239407,0.005751959,0.001085838,0.001963821,0.002191809],"category_scores_gemma":[0.03383192,0.0007632283,0.0006810779,0.002644096,0.007572033,0.01507603,0.005688746,0.003051142,0.0006147827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546957,"about_ca_system_score_gemma":0.0009235739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187435,"about_ca_topic_score_gemma":0.002273737,"domain_scores_codex":[0.9946554,0.002476793,0.000256121,0.001006617,0.00115841,0.0004466101],"domain_scores_gemma":[0.9694844,0.01585034,0.005606745,0.004871648,0.002355121,0.001831817],"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":[0.0001028286,0.00009719554,0.04340276,0.0003414659,0.0001064515,0.001523105,0.03071282,0.01050402,0.008840944,0.8122968,0.005741057,0.08633053],"study_design_scores_gemma":[0.0000346125,0.0000785272,0.0354516,0.0002558303,0.00005906,0.001502099,0.01068115,0.04999148,0.003072426,0.8279167,0.07077878,0.0001777382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5510563,0.003735338,0.3758959,0.009783297,0.0003465857,0.0003141568,0.0009797119,0.0008189618,0.05706971],"genre_scores_gemma":[0.9624084,0.0009607187,0.03236249,0.0003411682,0.0001786124,0.0001570759,0.0002785306,0.0001143108,0.003198562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005751959,"threshold_uncertainty_score":0.02132452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02758736508287792,"score_gpt":0.284811913609715,"score_spread":0.2572245485268371,"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."}}