  With the development of social networks, fake news for various commercial and
political purposes has been appearing in large numbers and gotten widespread in
the online world. With deceptive words, people can get infected by the fake
news very easily and will share them without any fact-checking. For instance,
during the 2016 US president election, various kinds of fake news about the
candidates widely spread through both official news media and the online social
networks. These fake news is usually released to either smear the opponents or
support the candidate on their side. The erroneous information in the fake news
is usually written to motivate the voters' irrational emotion and enthusiasm.
Such kinds of fake news sometimes can bring about devastating effects, and an
important goal in improving the credibility of online social networks is to
identify the fake news timely. In this paper, we propose to study the fake news
detection problem. Automatic fake news identification is extremely hard, since
pure model based fact-checking for news is still an open problem, and few
existing models can be applied to solve the problem. With a thorough
investigation of a fake news data, lots of useful explicit features are
identified from both the text words and images used in the fake news. Besides
the explicit features, there also exist some hidden patterns in the words and
images used in fake news, which can be captured with a set of latent features
extracted via the multiple convolutional layers in our model. A model named as
TI-CNN (Text and Image information based Convolutinal Neural Network) is
proposed in this paper. By projecting the explicit and latent features into a
unified feature space, TI-CNN is trained with both the text and image
information simultaneously. Extensive experiments carried on the real-world
fake news datasets have demonstrate the effectiveness of TI-CNN.
