{"type":"ScholarlyArticle","title":"GANs for Semi-Supervised Opinion Spam Detection","authors":[{"name":"Gray Stanton","slug":"gray-stanton","given":"Gray","family":"Stanton","affiliation":"Department of Statistics , Colorado State University"},{"name":"Athirai A. Irissappane","slug":"athirai-a-irissappane","given":"Athirai","family":"A. Irissappane","affiliation":"School of Engineering and Technology , University of Washington , Tacoma"}],"abstract":"Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews, only a few of them have been labeled spam or non-spam. We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor data show that spamGAN outperforms existing techniques when labeled data is limited. spamGAN can also generate reviews with reasonable perplexity.","doi":"10.24963/ijcai.2019/723","url":"https://ijcai.org/proceedings/2019/723","pdf":"https://ijcai.org/proceedings/2019/0723.pdf","markdown":"https://ijcai.org/proceedings/2019/723.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/723","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":723,"pages":{"first":5204,"last":5210},"keywords":["Natural Language Processing: Text Classification","Machine Learning: Deep Learning","Multidisciplinary Topics and Applications: Security and Privacy"],"videos":[],"references":[{"position":1,"raw":"[Crawford et al., 2015] Michael Crawford, Taghi M Khosh- goftaar, Joseph D Prusa, Aaron N Richter, and Hamzah Al Najada. Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1):23, 2015.","title":"Survey of review spam detection using machine learning techniques","authors":"Michael Crawford, M Taghi, Joseph D Khoshgoftaar, Aaron N Prusa, Hamzah Richter, Najada Al","year":"2015","venue":"Journal of Big Data","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fedus et al., 2018] William Fedus, Ian Goodfellow, and An- drew M Dai. Maskgan: Better text generation via filling in the . ICLR, 2018.","title":"Maskgan: Better text generation via filling in the","authors":"William Fedus, Ian Goodfellow, Andrew M Dai","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Feng et al., 2012] Song Feng, Ritwik Banerjee, and Yejin Choi. Syntactic stylometry for deception detection. In ACL, 2012.","title":"Syntactic stylometry for deception detection","authors":"Song Feng, Ritwik Banerjee, Yejin Choi","year":"2012","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Grover et al., 2018] Aditya Grover, Manik Dhar, and Ste- fano Ermon. Flow-gan: Combining maximum likelihood and adversarial learning in generative models. In AAAI, 2018.","title":"Flow-gan: Combining maximum likelihood and adversarial learning in generative models","authors":"Aditya Grover, Manik Dhar, Stefano Ermon","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Guo et al., 2018] Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. Long text generation via adversarial training with leaked information. In AAAI, 2018.","title":"Long text generation via adversarial training with leaked information","authors":"Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, Jun Wang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hernández et al., 2013] Donato Hernández, Rafael Guzmán, Manuel Móntes y Gomez, and Paolo Rosso. Using pu- learning to detect deceptive opinion spam. In Workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 38-45, 2013.","title":"Using pulearning to detect deceptive opinion spam","authors":"Donato Hernández, Rafael Guzmán, Manuel Móntes Y Gomez, Paolo Rosso","year":"2013","venue":"Workshop on computational approaches to subjectivity, sentiment and social media analysis","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2017] Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. Toward controlled generation of text. arXiv preprint arXiv:1703.00955, 2017.","title":"Toward controlled generation of text","authors":"Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P Xing","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huszár, 2015] Ferenc Huszár. How (not) to train your gener- ative model: Scheduled sampling, likelihood, adversary? arXiv preprint arXiv:1511.05101, 2015.","title":"Ferenc Huszár. How (not) to train your generative model: Scheduled sampling, likelihood, adversary","authors":"Huszár","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jindal and Liu, 2008] Nitin Jindal and Bing Liu. Opinion spam and analysis. In WSDM, 2008.","title":"Opinion spam and analysis","authors":"Nitin Jindal, Bing Liu","year":"2008","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jindal et al., 2010] Nitin Jindal, Bing Liu, and Ee-Peng Lim. Finding unusual review patterns using unexpected rules. In CIKM, 2010.","title":"Finding unusual review patterns using unexpected rules","authors":"Nitin Jindal, Bing Liu, Ee-Peng Lim","year":"2010","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Konda and Tsitsiklis, 2000] Vijay R Konda and John N Tsit- siklis. Actor-critic algorithms. In NIPS, 2000.","title":"Actor-critic algorithms","authors":"R Vijay, John N Konda, Tsitsiklis","year":"2000","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kumar et al., 2017] Abhishek Kumar, Prasanna Sattigeri, and Tom Fletcher. Semi-supervised learning with gans: manifold invariance with improved inference. In NIPS, 2017.","title":"Semi-supervised learning with gans: manifold invariance with improved inference","authors":"Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lai et al., 2015] Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. Recurrent convolutional neural networks for text classification. In AAAI, 2015.","title":"Recurrent convolutional neural networks for text classification","authors":"Siwei Lai, Liheng Xu, Kang Liu, Jun Zhao","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[LeCun et al., 2015] Yann LeCun, Yoshua Bengio, and Geof- frey Hinton. Deep learning. Nature, 521(7553):436, 2015.","title":"Deep learning","authors":"Yann Lecun, Yoshua Bengio, Geoffrey Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2011] Fangtao Li, Minlie Huang, Yi Yang, and Xi- aoyan Zhu. Learning to identify review spam. In IJCAI, 2011.","title":"Learning to identify review spam","authors":"Fangtao Li, Minlie Huang, Yi Yang, Xiaoyan Zhu","year":"2011","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2014] Huayi Li, Bing Liu, Arjun Mukherjee, and Ji- dong Shao. Spotting fake reviews using positive-unlabeled learning. Computación y Sistemas, 18(3):467-475, 2014.","title":"Spotting fake reviews using positive-unlabeled learning","authors":"Huayi Li, Bing Liu, Arjun Mukherjee, Jidong Shao","year":"2014","venue":"Computación y Sistemas","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2015] Huayi Li, Zhiyuan Chen, Arjun Mukherjee, Bing Liu, and Jidong Shao. Analyzing and detecting opin- ion spam on a large-scale dataset via temporal and spatial patterns. In AAAI-ICWSM, 2015.","title":"Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns","authors":"Li","year":"2015","venue":"AAAI-ICWSM","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2017] Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky. Adversarial Learning for Neural Dialogue Generation. 2017.","title":"Adversarial Learning for Neural Dialogue Generation","authors":"Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2018] Yang Li, Quan Pan, Suhang Wang, Tao Yang, and Erik Cambria. A generative model for category text generation. Information Sciences, 450:301-315, 2018.","title":"A generative model for category text generation","authors":"Yang Li, Quan Pan, Suhang Wang, Tao Yang, Erik Cambria","year":"2018","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Mukherjee et al., 2013] Arjun Mukherjee, Vivek Venkatara- man, Bing Liu, and Natalie Glance. What yelp fake review filter might be doing? In AAAI-ICWSM, 2013.","title":"What yelp fake review filter might be doing?","authors":"Arjun Mukherjee, Vivek Venkataraman, Bing Liu, Natalie Glance","year":"2013","venue":"AAAI-ICWSM","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ott et al., 2011] Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. Finding deceptive opinion spam by any stretch of the imagination. In ACL, 2011.","title":"Finding deceptive opinion spam by any stretch of the imagination","authors":"Myle Ott, Yejin Choi, Claire Cardie, Jeffrey T Hancock","year":"2011","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Rayana and Akoglu, 2015] Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging re- view networks and metadata. In KDD, 2015.","title":"Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging review networks and metadata","authors":"Akoglu Rayana","year":"2015","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ren and Ji, 2017] Yafeng Ren and Donghong Ji. Neural net- works for deceptive opinion spam detection: An empirical study. Information Sciences, 385:213-224, 2017.","title":"Neural networks for deceptive opinion spam detection: An empirical study","authors":"Ji ; Ren, Yafeng Ren, Donghong Ji","year":"2017","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Sutton et al., 2000] Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. Policy gradient methods for reinforcement learning with function approxi- mation. In NIPS, 2000.","title":"Policy gradient methods for reinforcement learning with function approximation","authors":"David A Richard S Sutton, Mcallester, P Satinder, Yishay Singh, Mansour","year":"2000","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tuan and Lee, 2018] Yi-Lin Tuan and Hung-Yi Lee. Improv- ing conditional sequence generative adversarial networks by stepwise evaluation. arXiv:1808.05599, 2018.","title":"Improving conditional sequence generative adversarial networks by stepwise evaluation","authors":"Yi-Lin Tuan, Hung-Yi Lee","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wasserman and Lafferty, 2008] Larry Wasserman and John D Lafferty. Statistical analysis of semi-supervised regression. In NIPS, 2008.","title":"Statistical analysis of semi-supervised regression","authors":"Lafferty Wasserman, Larry Wasserman, John D Lafferty","year":"2008","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Xu et al., 2015] Yinqing Xu, Bei Shi, Wentao Tian, and Wai Lam. A unified model for unsupervised opinion spamming detection incorporating text generality. In AAAI, 2015.","title":"A unified model for unsupervised opinion spamming detection incorporating text generality","authors":"Yinqing Xu, Bei Shi, Wentao Tian, Wai Lam","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yu et al., 2017] Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. In AAAI, 2017.","title":"Seqgan: Sequence generative adversarial nets with policy gradient","authors":"Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2018] Wen Zhang, Yuhang Du, Taketoshi Yoshida, and Qing Wang. Dri-rcnn: An approach to de- ceptive review identification using recurrent convolutional neural network. Information Processing & Management, 54(4):576-592, 2018.","title":"Dri-rcnn: An approach to deceptive review identification using recurrent convolutional neural network","authors":"Wen Zhang, Yuhang Du, Taketoshi Yoshida, Qing Wang","year":"2018","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}