Recent pandemics, such as H1N1 and COVID-19, have had extensive negative effects on the social and economic well-being of communities. Despite efforts to prevent and control their spread, governments have turned to a strategy of Living With the Virus to manage, rather than eliminate, the impact of these pandemics. However, group activities such as collaborative spatial crowdsourcing can still lead to the significant spread of infection due to the correlation between individuals’ mobility, interactions, and infection spread. In this paper, we address the problem of spatial crowdsourcing-induced infection spread and propose Epidemic-aware Maximum Task Assignment (EMTA). EMTA aims to form and assign collaborative worker groups to spatial crowdsourcing tasks while taking into consideration the control of epidemic spread. We prove that EMTA is NP-hard and inapproximable. We then propose the Epidemic-aware Task Assignment Algorithm (ETAA) that leverages epidemic characteristics to fully address EMTA. The experimental results from real LBSN and real epidemic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art baselines in terms of effectiveness and efficiency.

Real-timesocial interactions andmulti-streamingare two critical features oflive streaming services. In this paper, we formulate a new fundamental service query,Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and designSDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and proposeTDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies,PCPandCDPto boost SDSSel and TDSSel. We further propose a more challenging but practical service query,Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We designGPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services.

Epidemics such as COVID-19, SARS, H1N1 have highly transmissible viruses and spread wildly through the population with negative consequences. Multiple studies have shown the correlation between the contact networks between individuals and the transmission of infections due to contact between colocated individuals. To mitigate the transmission of the virus, intervention measures have been applied without decisive success. Therefore, reducing transmissions through suitable epidemicaware POI recommendations to users is necessary to cope with user mobility. Current POI recommendation approaches do not take into consideration the transmission of infections between co-located users. In this paper, we formulate a new query named Epidemic-aware POI Recommendation Query (EPQ), to timely recommend a set of POIs to users at different time steps, while considering the spread of infection between co-located users, their social friendships, and their preference. We prove that EPQ is NP-hard and propose an effective and efficient algorithm, Epidemic-aware POI Recommendation (EpRec) to tackle EPQ. We evaluate EpRec on existing location-based social networks and pandemic datasets against state-of-the-art algorithms. The experimental results show that EpRec outperforms the baselines in effectiveness and efficiency.

The popularity of live streaming has led to the explosive growth in new video contents and social communities on emerging platforms such as Facebook Live and Twitch. Viewers on these platforms are able to follow multiple streams of live events simultaneously, while engaging discussions with friends. However, existing approaches for selecting live streaming channels still focus on satisfying individual preferences of users, without considering the need to accommodate real-time social interactions among viewers and to diversify the content of streams. In this paper, therefore, we formulate a new Social-aware Diverse and Preferred Live Streaming Channel Query (SDSQ) that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. We perform a user study on Twitch with 432 participants to validate the need of SDSQ and the usefulness of SDSSel. We also conduct large-scale experiments on real datasets to demonstrate the superiority of the proposed algorithm over several baselines in terms of solution quality and efficiency.