Opinion spam detection: using multi-iterative graph-based model

The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called “Multi-iterative Graph-based opinion Spam Detection” (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entities’) relationships. The MGSD model is able to evaluate the ‘spamicity’ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material

Detecting Fake Reviews: Just a Matter of Data

Along with the ever-increasing portfolio of products online, the incentive for market participants to write fake reviews to gain a competitive edge has increased as well. This article demonstrates the effectiveness of using different combinations of spam detection features to detect fake reviews other than the review-based features typically used. Using a spectrum of feature sets offers greater accuracy in identifying fake reviews than using review-based features only, and using a machine learning algorithm for classification and different amounts of feature sets further elucidates the difference in performance. Results compared by benchmarking show that applying a technique prioritizing feature importance benefits from prioritizing features from multiple feature sets and that creating feature sets based on reviews, reviewers and product data can achieve the greatest accuracy

Fake Review Detection using Data Mining

Online spam reviews are deceptive evaluations of products and services. They are often carried out as a deliberate manipulation strategy to deceive the readers. Recognizing such reviews is an important but challenging problem. In this work, I try to solve this problem by using different data mining techniques. I explore the strength and weakness of those data mining techniques in detecting fake review. I start with different supervised techniques such as Support Vector Ma- chine (SVM), Multinomial Naive Bayes (MNB), and Multilayer Perceptron. The results attest that all the above mentioned supervised techniques can successfully detect fake review with more than 86% accuracy. Then, I work on a semi-supervised technique which reduces the dimension- ality of the input features vector but offers similar performance to existing approaches. I use a combination of topic modeling and SVM for the implementation of the semi-supervised tech- nique. I also compare the results with other approaches that consider all the words of a dataset as input features. I found that topic words are enough as input features to get similar accuracy compared to other approaches where researchers consider all the words as input features. At the end, I propose an unsupervised learning approach named as Words Basket Analysis for fake re- view detection. I utilize five Amazon products review dataset for an experiment and report the performance of the proposed on these datasets

Task-specific Word Identification from Short Texts Using a Convolutional Neural Network

Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries (e.g., WordNet), which are often unavailable for many applications such as social discrimination detection and fake review detection. However, we often have a set of labeled short texts where each short text has a task-related class label, e.g., discriminatory or non-discriminatory, specified by users or learned by classification algorithms. In this paper, we focus on identifying task-specific words and phrases from short texts by exploiting their class labels rather than using seed words or lexical dictionaries. We consider the task-specific word and phrase identification as feature learning. We train a convolutional neural network over a set of labeled texts and use score vectors to localize the task-specific words and phrases. Experimental results on sentiment word identification show that our approach significantly outperforms existing methods. We further conduct two case studies to show the effectiveness of our approach. One case study on a crawled tweets dataset demonstrates that our approach can successfully capture the discrimination-related words/phrases. The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa

Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?

[EN] Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves collecting (supposedly) truthful reviews online and adding them to a set of deceptive reviews obtained through crowdsourcing services. Models trained this way are generally successful at discriminating between `genuine¿ online reviews and the crowdsourced deceptive reviews. It has been argued that the deceptive reviews obtained via crowdsourcing are very different from real fake reviews, but the claim has never been properly tested. In this paper, we compare (false) crowdsourced reviews with a set of `real¿ fake reviews published on line. We evaluate their degree of similarity and their usefulness in training models for the detection of untrustworthy reviews. We find that the deceptive reviews collected via crowdsourcing are significantly different from the fake reviews published online. In the case of the artificially produced deceptive texts, it turns out that their domain similarity with the targets affects the models¿ performance, much more than their untruthfulness. This suggests that the use of crowdsourced datasets for opinion spam detection may not result in models applicable to the real task of detecting deceptive reviews. As an alternative method to create large-size datasets for the fake reviews detection task, we propose methods based on the probabilistic annotation of unlabeled texts, relying on the use of meta-information generally available on the e-commerce sites. Such methods are independent from the content of the reviews and allow to train reliable models for the detection of fake reviews.Leticia Cagnina thanks CONICET for the continued financial support. This work was funded by MINECO/FEDER (Grant No. SomEMBED TIN2015-71147-C2-1-P). The work of Paolo Rosso was partially funded by the MISMIS-FAKEnHATE Spanish MICINN research project (PGC2018-096212-B-C31). Massimo Poesio was in part supported by the UK Economic and Social Research Council (Grant Number ES/M010236/1).Fornaciari, T.; Cagnina, L.; Rosso, P.; Poesio, M. (2020). Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?. Language Resources and Evaluation. 54(4):1019-1058. https://doi.org/10.1007/s10579-020-09486-5S10191058544Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61(6), 54–61.Banerjee, S., & Chua, A. Y. (2014). Applauses in hotel reviews: Genuine or deceptive? In: Science and Information Conference (SAI), 2014 (pp. 938–942). New York: IEEE.Bhargava, R., Baoni, A., & Sharma, Y. (2018). Composite sequential modeling for identifying fake reviews. Journal of Intelligent Systems,. https://doi.org/10.1515/jisys-2017-0501.Bickel, P. J., & Doksum, K. A. (2015). Mathematical statistics: Basic ideas and selected topics (2nd ed., Vol. 1). Boca Raton: Chapman and Hall/CRC Press.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory (pp. 92–100). New York: ACM.Cagnina, L. C., & Rosso, P. (2017). Detecting deceptive opinions: Intra and cross-domain classification using an efficient representation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2), 151–174. https://doi.org/10.1142/S0218488517400165.Cardoso, E. F., Silva, R. M., & Almeida, T. A. (2018). Towards automatic filtering of fake reviews. Neurocomputing, 309, 106–116. https://doi.org/10.1016/j.neucom.2018.04.074.Carpenter, B. (2008). Multilevel bayesian models of categorical data annotation. Retrieved from http://lingpipe.files.wordpress.com/2008/11/carp-bayesian-multilevel-annotation.pdf.Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.Costa, P. T., & MacCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Professional manual. Psychological Assessment Resources.Dawid, A. P., & Skene, A. M. (1979). Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied Statistics, 28(1), 20–28.Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1), 1–38.Elkan, C., & Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 213–220). New York: ACM.Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (Vol. 13, pp. 175–184).Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Vol. 2: Short Papers, pp. 171–175). Jeju Island: Association for Computational Linguistics.Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3, 1289–1305.Fornaciari, T., & Poesio, M. (2013). Automatic deception detection in Italian court cases. Artificial intelligence and law, 21(3), 303–340. https://doi.org/10.1007/s10506-013-9140-4.Fornaciari, T., & Poesio, M. (2014). Identifying fake amazon reviews as learning from crowds. In: Proceedings of the 14th conference of the European chapter of the Association for Computational Linguistics (pp. 279–287). Gothenburg: Association for Computational Linguistics. Retrieved from http://www.aclweb.org/anthology/E14-1030.Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models., Analytical methods for social research Cambridge: Cambridge University Press.Graves, A., Jaitly, N., & Mohamed, A. R. (2013). Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding (ASRU) (pp. 273–278). New York: IEEE.Hernández-Castañeda, Á., & Calvo, H. (2017). Deceptive text detection using continuous semantic space models. Intelligent Data Analysis, 21(3), 679–695.Hernández Fusilier, D., Guzmán, R., Móntes y Gomez, M., & Rosso, P. (2013). Using pu-learning to detect deceptive opinion spam. In: Proc. of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 38–45).Hernández Fusilier, D., Montes-y Gómez, M., Rosso, P., & Cabrera, R. G. (2015). Detecting positive and negative deceptive opinions using pu-learning. Information Processing & Management, 51(4), 433–443.Hovy, D. (2016). The enemy in your own camp: How well can we detect statistically-generated fake reviews–an adversarial study. In: The 54th annual meeting of the association for computational linguistics (p 351).Jelinek, F., Lafferty, J. D., & Mercer, R. L. (1992). Basic methods of probabilistic context free grammars. Speech recognition and understanding (pp. 345–360). New York: Springer.Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining (pp. 219–230). New York: ACM.Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software, 15(9), 1–28.Kim, S., Lee, S., Park, D., & Kang, J. (2017). Constructing and evaluating a novel crowdsourcing-based paraphrased opinion spam dataset. In: Proceedings of the 26th international conference on world wide web (pp. 827–836). Geneva: International World Wide Web Conferences Steering Committee.Li, F., Huang, M., Yang, Y., & Zhu, X. (2011). Learning to identify review spam. IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 22(3), 2488–2493.Li, H., Chen, Z., Liu, B., Wei, X., & Shao, J. (2014a). Spotting fake reviews via collective positive-unlabeled learning. In: 2014 IEEE international conference on data mining (ICDM) (pp. 899–904). New York: IEEE.Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017). Bimodal distribution and co-bursting in review spam detection. In: Proceedings of the 26th international conference on world wide web (pp. 1063–1072). Geneva: International World Wide Web Conferences Steering Committee.Li, H., Liu, B., Mukherjee, A., & Shao, J. (2014b). Spotting fake reviews using positive-unlabeled learning. Computación y Sistemas, 18(3), 467–475.Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014c). Towards a general rule for identifying deceptive opinion spam. In: ACL (Vol. 1, pp. 1566–1576).Lin, C. H., Hsu, P. Y., Cheng, M. S., Lei, H. T., & Hsu, M. C. (2017). Identifying deceptive review comments with rumor and lie theories. In: International conference in swarm intelligence (pp. 412–420). New York: Springer.Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using positive and unlabeled examples. In: Third IEEE international conference on data mining (pp. 179–186). New York: IEEE.Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text documents. ICML, 2, 387–394.Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering,. https://doi.org/10.1007/s10664-019-09706-9.Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781.Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013a). Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 632–640) New York: ACM.Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. S. (2013b). What yelp fake review filter might be doing? In: Proceedings of the seventh international AAAI conference on weblogs and social media.Negri, M., Bentivogli, L., Mehdad, Y., Giampiccolo, D., & Marchetti, A. (2011). Divide and conquer: Crowdsourcing the creation of cross-lingual textual entailment corpora. In: Proceedings of the conference on empirical methods in natural language processing (pp. 670–679). Stroudsburg: Association for Computational Linguistics.Ott, M., Cardie, C., & Hancock, J. T. (2013). Negative deceptive opinion spam. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 497–501).Ott, M., Choi, Y., Cardie, C., & Hancock, J. (2011). Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual meeting of the association for computational linguistics: human language technologies (pp. 309–319). Portland, Oregon: Association for Computational Linguistics.Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count (LIWC): LIWC2001. Mahwah: Lawrence Erlbaum Associates.Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543).Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., et al. (2010). Learning from crowds. Journal of Machine Learning Research, 11, 1297–1322.Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An empirical study. Information Sciences, 385, 213–224.Rout, J. K., Dalmia, A., Choo, K. K. R., Bakshi, S., & Jena, S. K. (2017). Revisiting semi-supervised learning for online deceptive review detection. IEEE Access, 5(1), 1319–1327.Saini, M., & Sharan, A. (2017). Ensemble learning to find deceptive reviews using personality traits and reviews specific features. Journal of Digital Information Management, 12(2), 84–94.Salloum, W., Edwards, E., Ghaffarzadegan, S., Suendermann-Oeft, D., & Miller, M. (2017). Crowdsourced continuous improvement of medical speech recognition. In: The AAAI-17 workshop on crowdsourcing, deep learning, and artificial intelligence agents.Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In: Proceedings of international conference on new methods in language processing. Retrieved from http://www.ims.uni-stuttgart.de/ftp/pub/corpora/tree-tagger1.pdf.Shehnepoor, S., Salehi, M., Farahbakhsh, R., & Crespi, N. (2017). Netspam: A network-based spam detection framework for reviews in online social media. IEEE Transactions on Information Forensics and Security, 12(7), 1585–1595.Skeppstedt, M., Peldszus, A., & Stede, M. (2018). More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing. In: Proceedings of the 5th workshop on argument mining (pp. 155–163).Strapparava, C., & Mihalcea, R. (2009). The lie detector: Explorations in the automatic recognition of deceptive language. In: Proceedings of the 47th annual meeting of the association for computational linguistics and the 4th international joint conference on natural language processing.Streitfeld, D. (August $$25{{\rm th}}$$, 2012). The best book reviews money can buy. The New York Times.Whitehill, J., Wu, T., Bergsma, F., Movellan, J. R., & Ruvolo, P. L. (2009). Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. Advances in neural information processing systems (pp. 2035–2043). Cambridge: MIT Press.Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012). Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 823–831). New York: ACM.Yang, Y., & Liu, X. (1999). A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’99 (pp. 42–49). New York: ACM.Zhang, W., Bu, C., Yoshida, T., & Zhang, S. (2016). Cospa: A co-training approach for spam review identification with support vector machine. Information, 7(1), 12.Zhang, W., Du, Y., Yoshida, T., & Wang, Q. (2018). DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Information Processing & Management, 54(4), 576–592.Zhou, L., Shi, Y., & Zhang, D. (2008). A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering, 20(8), 1077–1081

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1