THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: AN INTEGRATED LANGUAGE MODEL

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements

Designing Intelligent Expert Systems to Cope with Liars

To cope with the problem of input distortion by users of Web-based expert systems, we develop methods to distinguish liars from truth-tellers based on verifiable attributes, and redesign the expert systems to control the impact of input distortion. The four methods we propose are termed split tree, consolidated tree, value based split tree, and value based consolidated tree. They improve the performance of expert systems by improving accuracy or reduce misclassification cost. Numerical examples confirm that the most possible accurate recommendation is not always the most economical one. The recommendations based on minimizing misclassification costs are more moderate compared to that based on accuracy. In addition, the consolidated tree methods are more efficient than the split tree methods, since they do not always require the verification of attribute values

How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost

Input distortion is a common problem faced by expert systems, particularly those deployed with a Web interface. In this study, we develop novel methods to distinguish liars from truth-tellers, and redesign rule-based expert systems to address such a problem. The four proposed methods are termed split tree (ST), consolidated tree (CT), value-based split tree (VST), and value-based consolidated tree (VCT), respectively. Among them, ST and CT aim to increase an expert system’s accuracy of recommendations, and VST and VCT attempt to reduce the misclassification cost resulting from incorrect recommendations. We observe that ST and VST are less efficient than CT and VCT in that ST and VST always require selected attribute values to be verified, whereas CT and VCT do not require value verification under certain input scenarios. We conduct experiments to compare the performances of the four proposed methods and two existing methods, i.e., the traditional true tree (TT) method that ignores input distortion and the knowledge modification (KM) method proposed in prior research. The results show that CT and ST consistently rank first and second, respectively, in maximizing the recommendation accuracy, and VCT and VST always lead to the lowest and second lowest misclassification cost. Therefore, CT and VCT should be the methods of choice in dealing with users’ lying behaviors. Furthermore, we find that KM is outperformed by not only the four proposed methods, but sometimes even by the TT method. This result further confirms the advantage necessity of differentiating liars from truth-tellers when both types of users exist in the population

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

Detection of opinion spam with character n-grams

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18117-2_21In this paper we consider the detection of opinion spam as a stylistic classi cation task because, given a particular domain, the deceptive and truthful opinions are similar in content but di ffer in the way opinions are written (style). Particularly, we propose using character ngrams as features since they have shown to capture lexical content as well as stylistic information. We evaluated our approach on a standard corpus composed of 1600 hotel reviews, considering positive and negative reviews. We compared the results obtained with character n-grams against the ones with word n-grams. Moreover, we evaluated the e ffectiveness of character n-grams decreasing the training set size in order to simulate real training conditions. The results obtained show that character n-grams are good features for the detection of opinion spam; they seem to be able to capture better than word n-grams the content of deceptive opinions and the writing style of the deceiver. In particular, results show an improvement of 2:3% and 2:1% over the word-based representations in the detection of positive and negative deceptive opinions respectively. Furthermore, character n-grams allow to obtain a good performance also with a very small training corpus. Using only 25% of the training set, a Na ve Bayes classi er showed F1 values up to 0.80 for both opinion polarities.This work is the result of the collaboration in the frame-work of the WIQEI IRSES project (Grant No. 269180) within the FP7 Marie Curie. The second author was partially supported by the LACCIR programme under project ID R1212LAC006. Accordingly, the work of the third author was in the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge inTexts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Hernández Fusilier, D.; Montes Gomez, M.; Rosso, P.; Guzmán Cabrera, R. (2015). Detection of opinion spam with character n-grams. En Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II. Springer International Publishing. 285-294. https://doi.org/10.1007/978-3-319-18117-2_21S285294Blamey, B., Crick, T., Oatley, G.: RU:-) or:-(? character-vs. word-gram feature selection for sentiment classification of OSN corpora. Research and Development in Intelligent Systems XXIX, 207–212 (2012)Drucker, H., Wu, D., Vapnik, V.N.: Support Vector Machines for Spam Categorization. IEEE Transactions on Neural Networks 10(5), 1048–1054 (2002)Feng, S., Banerjee, R., Choi, Y.: Syntactic Stylometry for Deception Detection. Association for Computational Linguistics, short paper. ACL (2012)Feng, S., Xing, L., Gogar, A., Choi, Y.: Distributional Footprints of Deceptive Product Reviews. In: Proceedings of the 2012 International AAAI Conference on WebBlogs and Social Media (June 2012)Gyongyi, Z., Garcia-Molina, H., Pedersen, J.: Combating Web Spam with Trust Rank. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 576–587. VLDB Endowment (2004)Hall, M., Eibe, F., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: an Update. SIGKDD Explor. Newsl. 10–18 (2009)Hernández-Fusilier, D., Guzmán-Cabrera, R., Montes-y-Gómez, M., Rosso, P.: Using PU-learning to Detect Deceptive Opinion Spam. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, USA, pp. 38–45 (2013)Hernández-Fusilier, D., Montes-y-Gómez, M., Rosso, P., Guzmán-Cabrera, R.: Detecting Positive and Negative Deceptive Opinions using PU-learning. Information Processing & Management (2014), doi:10.1016/j.ipm.2014.11.001Jindal, N., Liu, B.: Opinion Spam and Analysis. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 219–230 (2008)Jindal, N., Liu, B., Lim, E.: Finding Unusual Review Patterns Using Unexpected Rules. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 210–220(October 2010)Kanaris, I., Kanaris, K., Houvardas, I., Stamatatos, E.: Word versus character n-grams for anti-spam filtering. International Journal on Artificial Intelligence Tools 16(6), 1047–1067 (2007)Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting Product Review Spammers Using Rating Behaviours. In: CIKM, pp. 939–948 (2010)Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lecture on Human Language Technologies. Morgan & Claypool Publishers (2012)Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting Group Review Spam. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 93–94 (2011)Ntoulas, A., Najork, M., Manasse, M., Fetterly, D.: Detecting Spam Web Pages through Content Analysis. Transactions on Management Information Systems (TMIS), 83–92 (2006)Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: 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, Portland, Oregon, USA, pp. 309–319 (2011)Ott, M., Cardie, C., Hancock, J.T.: Negative Deceptive Opinion Spam. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, USA, pp. 309–319 (2013)Raymond, Y.K., Lau, S.Y., Liao, R., Chi-Wai, K., Kaiquan, X., Yunqing, X., Yuefeng, L.: Text Mining and Probabilistic Modeling for Online Review Spam Detection. ACM Transactions on Management Information Systems 2(4), Article: 25, 1–30 (2011)Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. Journal of Law & Policy 21(2) (2013)Wu, G., Greene, D., Cunningham, P.: Merging Multiple Criteria to Identify Suspicious Reviews. In: RecSys 2010, pp. 241–244 (2010)Xie, S., Wang, G., Lin, S., Yu, P.S.: Review Spam Detection via Time Series Pattern Discovery. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 635–636 (2012)Zhou, L., Sh, Y., Zhang, D.: A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering 20(8), 1077–1081 (2008

Analysis of malicious input issues on intelligent systems

Intelligent systems can facilitate decision making and have been widely applied to various domains. The output of intelligent systems relies on the users\u27 input. However, with the development of Web-Based Interface, users can easily provide dishonest input. Therefore, the accuracy of the generated decision will be affected. This dissertation presents three essays to discuss the defense solutions for malicious input into three types of intelligent systems: expert systems, recommender systems, and rating systems. Different methods are proposed in each domain based on the nature of each problem. The first essay addresses the input distortion issue in expert systems. It develops four methods to distinguish liars from truth-tellers, and redesign the expert systems to control the impact of input distortion by liars. Experimental results show that the proposed methods could lead to the better accuracy or the lower misclassification cost. The second essay addresses the shilling attack issue in recommender systems. It proposes an integrated Value-based Neighbor Selection (VNS) approach, which aims to select proper neighbors for recommendation systems that maximize the e-retailer\u27s profit while protecting the system from shilling attacks. Simulations are conducted to demonstrate the effectiveness of the proposed method. The third essay addresses the rating fraud issue in rating systems. It designs a two-phase procedure for rating fraud detection based on the temporal analysis on the rating series. Experiments based on the real-world data are utilized to evaluate the effectiveness of the proposed method

Detección de opinion spam usando PU-learning

Tesis por compendio[EN] Abstract The detection of false or true opinions about a product or service has become nowadays a very important problem. Recent studies show that up to 80% of people have changed their final decision on the basis of opinions checked on the web. Some of these opinions may be false, positive in order to promote a product/service or negative to discredit it. To help solving this problem in this thesis is proposed a new method for detection of false opinions, called PU-Learning*, which increases the precision by an iterative algorithm. It also solves the problem of lack of labeled opinions. To operate the method proposed only a small set of opinions labeled as positive and another large set of opinions unlabeled are needed. From this last set, missing negative opinions are extracted and used to achieve a two classes binary classification. This scenario has become a very common situation in the available corpora. As a second contribution, we propose a representation based on n-grams of characters. This representation has the advantage of capturing both the content and the writing style, allowing for improving the effectiveness of the proposed method for the detection of false opinions. The experimental evaluation of the method was carried out by conducting three experiments classification of opinions, using two different collections. The results obtained in each experiment allow seeing the effectiveness of proposed method as well as differences between the use of several types of attributes. Because the veracity or falsity of the reviews expressed by users becomes a very important parameter in decision making, the method presented here, can be used in any corpus where you have the above characteristics.[ES] Resumen La detección de opiniones falsas o verdaderas acerca de un producto o servicio, se ha convertido en un problema muy relevante de nuestra 'época. Según estudios recientes hasta el 80% de las personas han cambiado su decisión final basados en las opiniones revisadas en la web. Algunas de estas opiniones pueden ser falsas positivas, con la finalidad de promover un producto, o falsas negativas para desacreditarlo. Para ayudar a resolver este problema se propone en esta tesis un nuevo método para la detección de opiniones falsas, llamado PU-Learning modificado. Este método aumenta la precisión mediante un algoritmo iterativo y resuelve el problema de la falta de opiniones etiquetadas. Para el funcionamiento del método propuesto se utilizan un conjunto pequeño de opiniones etiquetadas como falsas y otro conjunto grande de opiniones no etiquetadas, del cual se extraen las opiniones faltantes y así lograr una clasificación de dos clases. Este tipo de escenario se ha convertido en una situación muy común en los corpus de opiniones disponibles. Como una segunda contribución se propone una representación basada en n-gramas de caracteres. Esta representación tiene la ventaja de capturar tanto elementos de contenido como del estilo de escritura, permitiendo con ello mejorar la efectividad del método propuesto en la detección de opiniones falsas. La evaluación experimental del método se llevó a cabo mediante tres experimentos de clasificación de opiniones utilizando dos colecciones diferentes. Los resultados obtenidos en cada experimento permiten ver la efectividad del método propuesto así como también las diferencias entre la utilización de varios tipos de atributos. Dado que la falsedad o veracidad de las opiniones vertidas por los usuarios, se convierte en un parámetro muy importante en la toma de decisiones, el método que aquí se presenta, puede ser utilizado en cualquier corpus donde se tengan las características mencionadas antes.[CA] Resum La detecció d'opinions falses o vertaderes al voltant d'un producte o servei s'ha convertit en un problema força rellevant de la nostra època. Segons estudis recents, fins el 80\% de les persones han canviat la seua decisió final en base a les opinions revisades en la web. Algunes d'aquestes opinions poden ser falses positives, amb la finalitat de promoure un producte, o falses negatives per tal de desacreditarlo. Per a ajudar a resoldre aquest problema es proposa en aquesta tesi un nou mètode de detecció d'opinions falses, anomenat PU-Learning*. Aquest mètode augmenta la precisió mitjançant un algoritme iteratiu i resol el problema de la falta d'opinions etiquetades. Per al funcionament del mètode proposat, s'utilitzen un conjunt reduït d'opinions etiquetades com a falses i un altre conjunt gran d'opinions no etiquetades, del qual se n'extrauen les opinions que faltaven i, així, aconseguir una classificació de dues classes. Aquest tipus d'escenari s'ha convertit en una situació molt comuna en els corpus d'opinions de què es disposa. Com una segona contribució es proposa una representació basada en n-gramas de caràcters. Aquesta representació té l'avantatge de capturar tant elements de contingut com a d'estil d'escriptura, permetent amb això millorar l'efectivitat del mètode proposat en la detecció d'opinions falses. L'avaluació experimental del mètode es va dur a terme mitjançant tres experiments de classificació d'opinions utilitzant dues coleccions diferents. Els resultats obtingut en cada experiment permeten veure l'efectivitat del mètode proposat, així com també les diferències entre la utilització de varis tipus d'atributs. Ja que la falsedat o veracitat de les opinions vessades pels usuaris es converteix en un paràmetre molt important en la presa de decisions, el mètode que ací es presenta pot ser utilitzat en qualsevol corpus on es troben les característiques abans esmentades.Hernández Fusilier, D. (2016). Detección de opinion spam usando PU-learning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61990TESISCompendi

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