BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software

Automatic Hoax Detection System

Hoaxes are non malicious viruses. They live on deceiving human's perception by conveying false claims as truth. Throughout history, hoaxes have actually able to influence a lot of people to the extent of tarnishing the victim's image and credibility. Moreover, wrong and misleading information has always been a distortion to a human's growth. Some hoaxes were created in a way that they can even obtain personal data by convincing the victims that those data were required for official purposes. Hoaxes are different from spams in a way that they masquerade themselves through the address of those related either directly or indirectly to us. Most of the time, they appear as a forwarded message and sometimes from legit companies such as PayPal. Having known the threat that this non malicious brought, it is important for us to address this problem seriously by providing an automatic hoax detection system as the solution to this matter. Consciousness and Awareness are definitely the first step to be taken for this matte

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

Investigation into the Application of Personality Insights and Language Tone Analysis in Spam Classification

Due to its persistence spam remains as one of the biggest problems facing users and suppliers of email communication services. Machine learning techniques have been very successful at preventing many spam mails from arriving in user mailboxes, however they still account for over 50% of all emails sent. Despite this relative success the economic cost of spam has been estimated as high as $50 billion in 2005 and more recently at$20 billion so spam can still be considered a considerable problem. In essence a spam email is a commercial communication trying to entice the receiver to take some positive action. This project uses the text from emails and creates personality insight and language tone scores through the use of IBM Watsons’ Tone Analyzer API. Those scores are used to investigate whether the language used in emails can be transformed into useful features that can be used to correctly classify them as spam or genuine emails. And during the course of this investigation a range of machine learning techniques are applied. Results from this experiment found that where just the personality insight and language tone features are used in the model some promising results with one dataset were shown. However over all datasets results were inconclusive with this model. Furthermore it was found that in a model where these features were used in combination with a normalised term-frequency feature-set no real improvement in the classification performance was shown

Character-level Convolutional Networks for Text Classification

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.Comment: An early version of this work entitled "Text Understanding from Scratch" was posted in Feb 2015 as arXiv:1502.01710. The present paper has considerably more experimental results and a rewritten introduction, Advances in Neural Information Processing Systems 28 (NIPS 2015