Based on the pattern of difference in Chinese social trust, this study classifies the social trust into trust in family members, trust in acquaintances, and trust in strangers. Then, the correlational relationship between different types of social trust and subjective well-being is examined using the micro survey data in China. It is found that different types of social trust vary greatly in the correlation with subjective well-being. The main findings are as follows: (a) Trust in family members has no significant correlation with subjective well-being; (b) Only “totally trust acquaintances” has a significant positive correlation with subjective well-being; (c) Trust in strangers has a significant positive correlation with subjective well-being—the higher the trust level, the stronger the correlation with subjective well-being will be—and (d) Urban–rural and male–female differences exist in the correlational relationship between trust in strangers and subjective well-being.

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Event detection in online social media has primarily focused on identifying abnormal spikes, or bursts, in activity. However, disruptive events such as socio-economic disasters, civil unrest, and even power outages, often involve abnormal troughs or lack of activity, leading to absenteeism. We present the first study, to our knowledge, that models absenteeism and uses detected absenteeism instances as a basis for event detection in location-based social networks such as Twitter. The proposed framework addresses the challenges of (i) early detection of absenteeism, (ii) identifying the locus of the absenteeism, and (iii) identifying groups or communities underlying the absenteeism. Our approach uses the formalism of graph wavelets to represent the spatiotemporal structure of user activity in a location-based social network. This formalism facilitates multiscale analysis, enabling us to detect anomalous behavior at different graph resolutions, which in turn allows the identification of event locations and underlying groups. The effectiveness of our approach is evaluated using Twitter activity related to civil unrest events in Latin America.