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Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students’ social network. [ABSTRACT FROM AUTHOR]