標題: Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions
作者: Shuai, Hong-Han
Lien, Yen-Chieh
Yang, De-Nian
Lan, Yi-Feng
Lee, Wang-Chien
Yu, Philip S.
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Social network analysis;online intervention;addiction
公開日期: 1-Jan-2018
摘要: While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models.
URI: http://dx.doi.org/10.1145/3269206.3271689
http://hdl.handle.net/11536/150983
DOI: 10.1145/3269206.3271689
期刊: CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
起始頁: 597
結束頁: 606
Appears in Collections:Conferences Paper