Online deception detection refueled by real world data collection Academic Article uri icon


  • 2018 Association for Computational Linguistics (ACL). All rights reserved. The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews 1 from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.

published proceedings

  • International Conference Recent Advances in Natural Language Processing, RANLP

author list (cited authors)

  • Yao, W., Dai, Z., Huang, R., & Caverlee, J.

complete list of authors

  • Yao, W||Dai, Z||Huang, R||Caverlee, J

publication date

  • January 2017