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Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions by Hong-Han Shuai, Yen-Chieh Lien, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, and Philip S. Yu is a scholarly article available to read on EtoBox.

What is Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions about?

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. Acco

Author

Hong-Han Shuai, Yen-Chieh Lien, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, and Philip S. Yu

Publisher

ACM

Published

2018

Language

EN