@InProceedings{chen-EtAl:2016:COLING3,
  author    = {Chen, Chengyao  and  Wang, Zhitao  and  Lei, Yu  and  Li, Wenjie},
  title     = {Content-based Influence Modeling for Opinion Behavior Prediction},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2207--2216},
  abstract  = {Nowadays, social media has become a popular platform for companies to
	understand their customers. It provides valuable opportunities to gain new
	insights into how a person's opinion about a product is influenced by his
	friends. Though various approaches have been proposed to study the opinion
	formation problem, they all formulate opinions as the derived sentiment values
	either discrete or continuous without considering the semantic information. In
	this paper, we propose a Content-based Social Influence Model to study the
	implicit mechanism underlying the change of opinions. We then apply the learned
	model to predict users' future opinions. The advantages of the proposed model
	is the ability to handle the semantic information and to learn two influence
	components including the opinion influence of the content information and the
	social relation factors. In the experiments conducted on Twitter datasets, our
	model significantly outperforms other popular opinion formation models.},
  url       = {http://aclweb.org/anthology/C16-1208}
}