We mind your well-being: Preventing depression in uncertain social networks by sequential interventions

• Main Authors: AUNG, Aye Phye Phye, WANG, Xinrun, AN, Bo, LI, Xiaoli
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Applied Behavior Analysis; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/9145; https://ink.library.smu.edu.sg/context/sis_research/article/10148/viewcontent/Mind_Well_being_pvoa.pdf
• Institution: Singapore Management University

Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale up to solve problems with a large number of participants due to the huge state space. Our contributions in this paper are fourfold. Firstly, we propose a new model that addresses the sequential intervention of participants while considering the propagation of emotions and formulate it as a Partially Observable Markov Decision Process (POMDP) to handle uncertainties about their mental states and the influence between them. Secondly, we apply reasoning to refine belief to improve solution quality for the lack of initial information on mental state values. Thirdly, we improve the scalability by the abstraction of states to reduce the number of states by representing the mental states with an abstracted discrete set. We further improve the scalability by multi-level partitioning to get smaller POMDPs. Finally, we conduct extensive experiments on both synthetic and real networks to show that our algorithm significantly improves scalability with comparable solution quality compared to the state-of-the-art algorithms.