Location based social network services like Facebook and Twitter have supported billions of social media users to share their check-ins all over the world. The massive check-in data is regarded as a kind of novel data resource to explore human mobility. In this paper, we study the human mobility characteristics and differences presented in Sina Weibo (a Chinese equivalent of Twitter) check-in data for different groups. First, we identified different groups based on their spatial distribution characters. Then, we selected two urban resident groups and two college student groups as our study objects. The four groups are consisted by more than 12,000 Sina Weibo users who contributed over 80,000 geo-tagged Weibo messages in Wuhan city from 2015-2016. We analyzed the four groups' mobility characters and patterns through spatiotemporal statistics and calculation. The quantitative analysis methods help us to find out that:(i) the mobility differences among communities can be observed through their check-ins;(ii) human dynamics and mobilities are largely affected by the distance (iii) similar social structure directed similar behavior patterns.
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