  In this paper, we present a three-step methodological framework, including
location identification, bias modification, and out-of-sample validation, so as
to promote human mobility analysis with social media data. More specifically,
we propose ways of identifying personal activity-specific places and commuting
patterns in Beijing, China, based on Weibo (China's Twitter) check-in records,
as well as modifying sample bias of check-in data with population synthesis
technique. An independent citywide travel logistic survey is used as the
benchmark for validating the results. Obvious differences are discerned from
Weibo users' and survey respondents' activity-mobility patterns, while there is
a large variation of population representativeness between data from the two
sources. After bias modification, the similarity coefficient between commuting
distance distributions of Weibo data and survey observations increases
substantially from 23% to 63%. Synthetic data proves to be a satisfactory
cost-effective alternative source of mobility information. The proposed
framework can inform many applications related to human mobility, ranging from
transportation, through urban planning to transport emission modelling.
