{"id":"W2731802473","doi":"10.1145/3079628.3079652","title":"Towards Understanding Users' Motivation in a Q&A Social Network Using Social Influence and the Moderation by Culture","year":2017,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Moderation; Collectivism; Sustenance; Structural equation modeling; Social network (sociolinguistics); Social influence; Order (exchange); Individualism; Knowledge management; Computer science; Psychology; Social psychology; Social media; Business; World Wide Web; Political science; Machine learning","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":[0.007753971,0.000370066,0.0003608182,0.00200884,0.001324735,0.003438488,0.0005139009,0.0006350142,0.002281731],"category_scores_gemma":[0.03138223,0.000249986,0.0005761604,0.001483126,0.001549035,0.003965774,0.002063503,0.0008254353,0.0001854399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353499,"about_ca_system_score_gemma":0.001508172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024193,"about_ca_topic_score_gemma":0.01055714,"domain_scores_codex":[0.9953924,0.003308892,0.0001572663,0.0004137968,0.0004621911,0.0002655037],"domain_scores_gemma":[0.9634225,0.0270294,0.005295168,0.001251965,0.001867893,0.001133065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002304763,0.0004447823,0.8652174,0.0002181506,0.0002312996,0.0001007035,0.07319857,0.001341223,0.001886221,0.009509696,0.0004620557,0.04715936],"study_design_scores_gemma":[0.00004730041,0.0003751802,0.8828408,0.0001860457,0.0002638816,0.00006228881,0.0686174,0.02917745,0.00177186,0.01218429,0.004383832,0.00008972369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906355,0.00005724129,0.004453756,0.0004054767,0.000006677831,0.00007265108,0.0000570146,0.0000161749,0.004295567],"genre_scores_gemma":[0.9981608,0.00002961687,0.001477973,0.00003117045,0.000003202792,0.00005946674,0.00001899493,0.000004046454,0.0002146145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01024193,"threshold_uncertainty_score":0.0410074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06563153241639223,"score_gpt":0.2914854130692564,"score_spread":0.2258538806528642,"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."}}