Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage. , software reuse, and online software development environment. He has published more than 50 papers in prestigious conferences and journals, such as ICWS, UbiComp, ICSP and etc. As a technical leader and manager, he has accomplished several key national projects on software engineering and smart cities. Cooperating with major smart-city solution providing companies, his research work has been adopted in more than 20 cities in China.

Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.

Job burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people's physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention.

With the increasing social acceptance and openness, more and more sexual-minority men (SMM) have succeeded in creating and sustaining steady relationships in recent years. Maintaining steady relationships is beneficial to the wellbeing of SMM both mentally and physically. However, the relationship maintaining for them is also challenging due to the much less supports compared to the heterosexual couples, so that it is important to identify those SMM in steady relationship and provide corresponding personalized assistance. Furthermore, knowing SMM's relationship and the correlations with other visible features is also beneficial for optimizing the social applications' functionalities in terms of privacy preserving and friends recommendation. With the prevalence of SMM-oriented social apps (called SMMSA for short), this paper investigates the relationship status of SMM from a new perspective, that is, by introducing the SMM's online digital footprints left on SMMSA (e.g., presented profile, social interactions, expressions, sentiment, and mobility trajectories). Specifically, using a filtered dataset containing 2,359 active SMMSA users with their self-reported relationship status and publicly available app usage data, we explore the correlations between SMM's relationship status and their online digital footprints on SMMSA and present a set of interesting findings. Moreover, we demonstrate that by utilizing such correlations, it has the potential to construct machine-learning-based models for relationship status inference. Finally, we elaborate on the implications of our findings from the perspective of better understanding the SMM community and improving their social welfare. CCS Concepts: • Information systems → Data mining; • Human-centered computing → User models; • Social and professional topics → Relationship Status.