@InProceedings{you-qian-liu:2018:C18-1,
  author    = {You, Zhenni  and  Qian, Tieyun  and  Liu, Bing},
  title     = {An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1884--1895},
  abstract  = {Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework.},
  url       = {http://www.aclweb.org/anthology/C18-1160}
}