  With the rapid growth of social media, rumors are also spreading widely on
social media and bring harm to people's daily life. Nowadays, information
credibility evaluation has drawn attention from academic and industrial
communities. Current methods mainly focus on feature engineering and achieve
some success. However, feature engineering based methods require a lot of labor
and cannot fully reveal the underlying relations among data. In our viewpoint,
the key elements of user behaviors for evaluating credibility are concluded as
"who", "what", "when", and "how". These existing methods cannot model the
correlation among different key elements during the spreading of microblogs. In
this paper, we propose a novel representation learning method, Information
Credibility Evaluation (ICE), to learn representations of information
credibility on social media. In ICE, latent representations are learnt for
modeling user credibility, behavior types, temporal properties, and comment
attitudes. The aggregation of these factors in the microblog spreading process
yields the representation of a user's behavior, and the aggregation of these
dynamic representations generates the credibility representation of an event
spreading on social media. Moreover, a pairwise learning method is applied to
maximize the credibility difference between rumors and non-rumors. To evaluate
the performance of ICE, we conduct experiments on a Sina Weibo data set, and
the experimental results show that our ICE model outperforms the
state-of-the-art methods.
