  Mining user opinion from Micro-Blogging has been extensively studied on the
most popular social networking sites such as Twitter and Facebook in the U.S.,
but few studies have been done on Micro-Blogging websites in other countries
(e.g. China). In this paper, we analyze the social opinion influence on
Tencent, one of the largest Micro-Blogging websites in China, endeavoring to
unveil the behavior patterns of Chinese Micro-Blogging users. This paper
proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously
incorporates topic factor and social direct influence in a unified
probabilistic framework. Based on TOIM, two topic level opinion influence
propagation and aggregation algorithms are developed to consider the indirect
influence: CP (Conservative Propagation) and NCP (None Conservative
Propagation). Users' historical social interaction records are leveraged by
TOIM to construct their progressive opinions and neighbors' opinion influence
through a statistical learning process, which can be further utilized to
predict users' future opinions on some specific topics. To evaluate and test
this proposed model, an experiment was designed and a sub-dataset from Tencent
Micro-Blogging was used. The experimental results show that TOIM outperforms
baseline methods on predicting users' opinion. The applications of CP and NCP
have no significant differences and could significantly improve recall and
F1-measure of TOIM.
