22,363 research outputs found
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to o er gen- erous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce nan- cial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for culture planning in the city of Beijing. We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information to identify latent patterns of urban cultural interactions. Using the characteristic typologies of mobile user cul- tural activities emitted by the model, we determine the levels of demand for di erent types of cultural resources across urban areas. We then compare those with the corresponding levels of supply as driven by the presence and spatial reach of cultural venues in local areas to obtain high resolution maps that indicate urban re- gions with lack or oversupply of cultural resources, and thus give evidence and suggestions for further urban cultural planning and investment optimisation.Cambridge Trus
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