Deceptive reviews and sentiment polarity: Effective link by exploiting BERT

[1]  Arjun Mukherjee,et al.  Exploiting Burstiness in Reviews for Review Spammer Detection , 2021, ICWSM.

[2]  Raymond Chiong,et al.  Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features , 2021, Multimedia Tools and Applications.

[3]  Rohit Kumar Kaliyar,et al.  FakeBERT: Fake news detection in social media with a BERT-based deep learning approach , 2021, Multim. Tools Appl..

[4]  Razieh Nokhbeh Zaeem,et al.  On Sentiment of Online Fake News , 2020, 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[5]  Jamal Riffi,et al.  PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures , 2020, Expert Syst. Appl..

[6]  Leonardo Neves,et al.  TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification , 2020, FINDINGS.

[7]  R. Zafarani,et al.  A Survey of Fake News , 2020, ACM Comput. Surv..

[8]  Yu Song,et al.  A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification , 2020, IEEE Access.

[9]  Huanhuan Chen,et al.  BDANN: BERT-Based Domain Adaptation Neural Network for Multi-Modal Fake News Detection , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[10]  Kilian Q. Weinberger,et al.  Revisiting Few-sample BERT Fine-tuning , 2020, ICLR.

[11]  Maksym Andriushchenko,et al.  On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines , 2020, ICLR.

[12]  Yongjun Hu,et al.  BERT-Based Mental Model, a Better Fake News Detector , 2020, ICCAI.

[13]  Xing Fang,et al.  Toward multi-label sentiment analysis: a transfer learning based approach , 2020, Journal of Big Data.

[14]  María del Rocío Martínez-Torres,et al.  A machine learning approach for the identification of the deceptive reviews in the hospitality sector using unique attributes and sentiment orientation , 2019, Tourism Management.

[15]  Kyunghyun Cho,et al.  Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models , 2019, ICLR.

[16]  Andrew McCarren,et al.  Fact or Factitious? Contextualized Opinion Spam Detection , 2019, ACL.

[17]  Athirai Aravazhi Irissappane,et al.  GANs for Semi-Supervised Opinion Spam Detection , 2019, IJCAI.

[18]  Roland Vollgraf,et al.  Contextual String Embeddings for Sequence Labeling , 2018, COLING.

[19]  Bing Liu,et al.  An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection , 2018, COLING.

[20]  Wen Zhang,et al.  DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network , 2018, Inf. Process. Manag..

[21]  Christopher Krügel,et al.  Detecting Deceptive Reviews Using Generative Adversarial Networks , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[22]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[23]  Naveen Kumar,et al.  Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning , 2018, J. Manag. Inf. Syst..

[24]  A. Flammini,et al.  The spread of low-credibility content by social bots , 2017, Nature Communications.

[25]  Yenewondim Biadgie,et al.  Detecting Negative Deceptive Opinion from Tweets , 2017 .

[26]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[27]  Ewa Maslowska,et al.  Do customer reviews drive purchase decisions? The moderating roles of review exposure and price , 2017, Decis. Support Syst..

[28]  Dong-Hong Ji,et al.  Neural networks for deceptive opinion spam detection: An empirical study , 2017, Inf. Sci..

[29]  Reza Farahbakhsh,et al.  NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media , 2017, IEEE Transactions on Information Forensics and Security.

[30]  Arjun Mukherjee,et al.  Detecting Sockpuppets in Deceptive Opinion Spam , 2017, CICLing.

[31]  Alexander F. Gelbukh,et al.  Cross-domain deception detection using support vector networks , 2016, Soft Computing.

[32]  Edward C. Malthouse,et al.  The Value of Online Customer Reviews , 2016, RecSys.

[33]  Arjun Mukherjee,et al.  On the Temporal Dynamics of Opinion Spamming: Case Studies on Yelp , 2016, WWW.

[34]  Joseph D. Prusa,et al.  Survey of review spam detection using machine learning techniques , 2015, Journal of Big Data.

[35]  Paolo Rosso,et al.  Classification of deceptive opinions using a low dimensionality representation , 2015, WASSA@EMNLP.

[36]  Leman Akoglu,et al.  Collective Opinion Spam Detection: Bridging Review Networks and Metadata , 2015, KDD.

[37]  Marie-Francine Moens,et al.  A survey on the application of recurrent neural networks to statistical language modeling , 2015, Comput. Speech Lang..

[38]  Alton Yeow-Kuan Chua,et al.  Using supervised learning to classify authentic and fake online reviews , 2015, IMCOM.

[39]  Alton Yeow-Kuan Chua,et al.  A theoretical framework to identify authentic online reviews , 2014, Online Inf. Rev..

[40]  Claire Cardie,et al.  Towards a General Rule for Identifying Deceptive Opinion Spam , 2014, ACL.

[41]  Kyumin Lee,et al.  The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing , 2014, ICWSM.

[42]  Claire Cardie,et al.  TopicSpam: a Topic-Model based approach for spam detection , 2013, ACL.

[43]  Arjun Mukherjee,et al.  What Yelp Fake Review Filter Might Be Doing? , 2013, ICWSM.

[44]  Claire Cardie,et al.  Negative Deceptive Opinion Spam , 2013, NAACL.

[45]  Christopher G. Harris.  Detecting Deceptive Opinion Spam Using Human Computation , 2012, HCOMP@AAAI.

[46]  Yejin Choi,et al.  Syntactic Stylometry for Deception Detection , 2012, ACL.

[47]  Yejin Choi,et al.  Distributional Footprints of Deceptive Product Reviews , 2012, ICWSM.

[48]  Grigorios Tsoumakas,et al.  On the Stratification of Multi-label Data , 2011, ECML/PKDD.

[49]  Yi Yang,et al.  Learning to Identify Review Spam , 2011, IJCAI.

[50]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[51]  Ee-Peng Lim,et al.  Finding unusual review patterns using unexpected rules , 2010, CIKM.

[52]  Derek Greene,et al.  Distortion as a validation criterion in the identification of suspicious reviews , 2010, SOMA '10.

[53]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[54]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[55]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[56]  Sung-Hyon Myaeng,et al.  Can You Distinguish Truthful from Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection , 2021, HCINLP.

[57]  Yaser Jararweh,et al.  Fake Reviews Detection: A Survey , 2021, IEEE Access.

[58]  Sindhu B. Hegde,et al.  Fake Review Detection Using Hybrid Ensemble Learning , 2021 .

[59]  Jiyun Zhou,et al.  T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information , 2021, ICANN.

[60]  Malika Ben Khalifa,et al.  Evidential Spammers and Group Spammers Detection , 2021, ISDA.

[61]  Xinhuai Tang,et al.  Fine-Tuning BERT for Multi-Label Sentiment Analysis in Unbalanced Code-Switching Text , 2020, IEEE Access.

[62]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[63]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.