ARTIFICIAL INTELLIGENCE\u27S ABILITY TO DETECT ONLINE PREDATORS

Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and grooming children without parents or law enforcement knowledge. Based on early research, one could assume that companies could do more to protect children online and that stricter online communication monitoring is needed. The research for this project covers the behaviors of predators, types of internet crimes against children and artificial intelligence’s ability to detect online predators with the end goal of protecting children online. The results from the research found that artificial intelligence can detect online predators in chats and image sharing. However, more research can be done on image detection. This paper will benefit parents looking to protect their children online, law enforcement agencies and companies that have active children on their websites or applications

Methodologies for the Management, Normalization and Identification of Sexual Predation of Minors in Cyber Chat Logs

Neural networks based on the Transformer architecture have shown great results in tasks such as machine translation and text generation. Our contribution provides a methodology for an AI agent capable of Sexual Predator Identification (SPI) based on the classification capabilities of models built on the Transformer architecture. Results are comparable to existing state-of-the-art methods, with a F0.5 score of 92.5% for predator identification on the PAN2012 test dataset consisting of 2,004,235 lines of text. Practical considerations require an AI agent that can evaluate large numbers of chats quickly. In that regard the Transformer based AI agent is able to evaluate over 2 million lines of text in under 6 minutes on a modestly configured workstation. An AI agent by itself does not provide a complete solution to sexual predator identification. In an effort to give practical value to an AI agent, we address the vitally important but often overlooked issues of chat management and normalization. Our contribution provides a methodology for efficiently transforming raw chats from a native format into a consistent 'normalized' format suitable for analysis. We define a methodology to the problem of managing large numbers of chats, converting/normalizing 10,000 documents in a dataset in under 3 minutes on a modestly configured workstation. We present a software-based solution that among other things brings together chat management, normalization, and AI based analysis into a cohesive, productive environment that law enforcement can use to identify and build a case against suspected predators

Online Sexual Predator Detection

Online sexual abuse is a concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning and deep learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose an approach that first tackles the problem of imbalance and then improves the effectiveness of the underlying classifiers. Our evaluation of the proposed sampling approach on PAN benchmark dataset shows performance improvements on several classification metrics, compared to prior methods that otherwise require hands-crafted sampling of the data

Identifying Online Sexual Predators Using Support Vector Machine

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The e↵ect of re-balancing the unbalanced dataset resulted in a better performance of the classifier. Testing the two-stage classification model on the unseen test set, 171 out of 254 predators are successfully identified giving a precision of 0.85, recall of 0.67 and f-score of 0.807. Comparing the classification performance with and without the behavioural feature, it can be seen the n-gram contributed the most to the performance of the classifier, while the behavioural features do not contribute significantly to the performance

Fine-Grained Analysis of Language Varieties and Demographics

[EN] The rise of social media empowers people to interact and communicate with anyone anywhere in the world. The possibility of being anonymous avoids censorship and enables freedom of expression. Nevertheless, this anonymity might lead to cybersecurity issues, such as opinion spam, sexual harassment, incitement to hatred or even terrorism propaganda. In such cases, there is a need to know more about the anonymous users and this could be useful in several domains beyond security and forensics such as marketing, for example. In this paper, we focus on a fine-grained analysis of language varieties while considering also the authors¿ demographics. We present a Low-Dimensionality Statistical Embedding method to represent text documents. We compared the performance of this method with the best performing teams in the Author Profiling task at PAN 2017. We obtained an average accuracy of 92.08% versus 91.84% for the best performing team at PAN 2017. We also analyse the relationship of the language variety identification with the authors¿ gender. Furthermore, we applied our proposed method to a more fine-grained annotated corpus of Arabic varieties covering 22 Arab countries and obtained an overall accuracy of 88.89%. We have also investigated the effect of the authors¿ age and gender on the identification of the different Arabic varieties, as well as the effect of the corpus size on the performance of our method.This publication was made possible by NPRP grant 9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Rangel, F.; Rosso, P.; Zaghouani, W.; Charfi, A. (2020). Fine-Grained Analysis of Language Varieties and Demographics. Natural Language Engineering. 26(6):641-661. https://doi.org/10.1017/S1351324920000108S641661266Kestemont, M. , Tschuggnall, M. , Stamatatos, E. , Daelemans, W. , Specht, G. , Stein, B. and Potthast, M. 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ARAP: Arabic Author Profiling Project for Cyber-Security. Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN).Agić, Ž. , Tiedemann, J. , Dobrovoljc, K. , Krek, S. , Merkler, D. , Može, S. , Nakov, P. , Osenova, P. and Vertan, C. (2014). Proceedings of the EMNLP 2014 Workshop on Language Technology for Closely Related Languages and Language Variants. Association for Computational Linguistics.Sadat, F., Kazemi, F., & Farzindar, A. (2014). Automatic Identification of Arabic Language Varieties and Dialects in Social Media. Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP). doi:10.3115/v1/w14-5904Franco-Salvador, M., Rangel, F., Rosso, P., Taulé, M., & Antònia Martít, M. (2015). Language Variety Identification Using Distributed Representations of Words and Documents. Experimental IR Meets Multilinguality, Multimodality, and Interaction, 28-40. doi:10.1007/978-3-319-24027-5_3Rosso, P., Rangel, F., Farías, I. H., Cagnina, L., Zaghouani, W., & Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass, 12(4), e12275. doi:10.1111/lnc3.12275Malmasi, S. , Zampieri, M. , Ljubešić, N. , Nakov, P. , Ali, A. and Tiedemann, J. (2016). Discriminating between similar languages and arabic dialect identification: A report on the third DSL shared task. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pp. 1–14.Rangel, F. , Rosso, P. , Potthast, M. and Stein, B. (2017). Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In Cappellato L., Ferro N., Goeuriot, L. and Mandl T. (eds), Working Notes Papers of the CLEF 2017 Evaluation Labs, p. 1613–0073, CLEF and CEUR-WS.org.Zampieri, M. , Malmasi, S. , Ljubešić, N. , Nakov, P. , Ali, A. , Tiedemann, J. , Scherrer, Y. , Aepli, N. (2017). 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Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning

Providing a safe environment for juveniles and children in online social networks is considered as a major factor in improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of online predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Sûreté du Québec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Sûreté du Québec

Recent trends in digital text forensics and its evaluation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40802-1_28This paper outlines the concepts and achievements of our evaluation lab on digital text forensics, PAN 13, which called for original research and development on plagiarism detection, author identification, and author profiling. We present a standardized evaluation framework for each of the three tasks and discuss the evaluation results of the altogether 58 submitted contributions. For the first time, instead of accepting the output of software runs, we collected the softwares themselves and run them on a computer cluster at our site. As evaluation and experimentation platform we use TIRA, which is being developed at the Webis Group in Weimar. TIRA can handle large-scale software submissions by means of virtualization, sandboxed execution, tailored unit testing, and staged submission. In addition to the achieved evaluation results, a major achievement of our lab is that we now have the largest collection of state-of-the-art approaches with regard to the mentioned tasks for further analysis at our disposal.This work was partially supported by the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action.Gollub, T.; Potthast, M.; Beyer, A.; Busse, M.; Rangel Pardo, FM.; Rosso, P.; Stamatatos, E.... (2013). Recent trends in digital text forensics and its evaluation. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 282-302. https://doi.org/10.1007/978-3-642-40802-1_28S282302Aleman, Y., Loya, N., Vilarino Ayala, D., Pinto, D.: Two Methodologies Applied to the Author Profiling Task—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Argamon, S., Juola, P.: Overview of the International Authorship Identification Competition at PAN-2011. In: Proc. of CLEF 2011 (2011)Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, Genre, and Writing Style in Formal Written Texts. TEXT 23, 321–346 (2003)Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically Profiling the Author of an Anonymous Text. Commun. ACM 52(2), 119–123 (2009)Armstrong, T.G., Moffat, A., Webber, W., Zobel, J.: EvaluatIR: An Online Tool for Evaluating and Comparing IR Systems. In: Proc. of SIGIR 2009 (2009)Blockeel, H., Vanschoren, J.: Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 6–17. Springer, Heidelberg (2007)Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating Gender on Twitter. In: Proc. EMNLP 2011 (2011)Clough, P., Stevenson, M.: Developing a Corpus of Plagiarised Short Answers. Lang. Resour. Eval. 45, 5–24 (2011)Clough, P., Gaizauskas, R., Piao, S.S.L., Wilks, Y.: METER: MEasuring TExt Reuse. In: Proc. ACL 2002 (2002)De Roure, D., Goble, C., Stevens, R.: The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows. Future Gener. Comp. Sy. 25, 561–567 (2009)Caurcel Diaz, A.A., Gomez Hidalgo, J.M.: Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Downie, J.S.: The Music Information Retrieval Evaluation Exchange (2005–2007): A Window into Music Information Retrieval Research. Acoust. Sc. and Tech. 29(4), 247–255 (2008)Hernandez Farias, D.I., Guzman-Cabrera, R., Reyes, A., Rocha, M.A.: Semantic-based Features for Author Profiling Identification: First Insights—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Flekova, L., Gurevych, I.: Can We Hide in the Web? Large Scale Simultaneous Age and Gender Author Profiling in Social Media–Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Forner, P., Navigli, R., Tufis, D. (eds.): CLEF 2013 Evaluation Labs and Workshop – Working Notes Papers (2013)Gillam, L.: Readability for author profiling?—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Gollub, T., Burrows, S., Stein, B.: First Experiences with TIRA for Reproducible Evaluation in Information Retrieval. In: Proc. of OSIR at SIGIR 2012 (August 2012)Gollub, T., Stein, B., Burrows, S.: Ousting Ivory Tower Research: Towards a Web Framework for Providing Experiments as a Service. In: Proc. of SIGIR 2012 (2012)Gollub, T., Stein, B., Burrows, S., Hoppe, D.: TIRA: Configuring, Executing, and Disseminating Information Retrieval Experiments. In: Proc. of TIR at DEXA 2012. IEEE (2012)Goswami, S., Sarkar, S., Rustagi, M.: Stylometric Analysis of Bloggers’ Age and Gender. In: Proc. of ICWSM 2009 (2009)Haggag, O., El-Beltagy, S.: Plagiarism Candidate Retrieval Using Selective Query Formulation and Discriminative Query Scoring—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Holmes, J., Meyerhoff, M.: The Handbook of Language and Gender. Blackwell Handbooks in Linguistics. Wiley (2003)Inches, G., Crestani, F.: Overview of the International Sexual Predator Identification Competition at PAN-2012. In: Proc. of CLEF 2012 (2012)Juola, P.: Authorship Attribution. Found. and Trends in IR 1, 234–334 (2008)Juola, P.: Ad-hoc Authorship Attribution Competition. In: Proc. of ALLC 2004 (2004)Juola, P.: An Overview of the Traditional Authorship Attribution Subtask. In: Proc. of CLEF 2012 (2012)Koppel, M., Winter, Y.: Determining if Two Documents are by the Same Author. Journal of the American Society for Information Science and Technology (to appear)Koppel, M., Argamon, S., Shimoni, A.R.: Automatically Categorizing Written Texts by Author Gender. Literary and Linguistic Computing 17(4), 401–412 (2002)Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring Differentiability: Unmasking Pseudonymous Authors. Journal of Machine Learning Research 8, 1261–1276 (2007)Koppel, M., Schler, J., Argamon, S.: Authorship Attribution in the Wild. Language Resources and Evaluation 45, 83–94 (2011)Kong, L., Qi, H., Du, C., Wang, M., Han, Z.: Approaches for Source Retrieval and Text Alignment of Plagiarism Detection—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Lim, W.Y., Goh, J., Thing, V.L.L.: Content-centric age and gender profiling—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Pastor Lopez-Monroy, A., Montes-Y-Gomez, M., Jair Escalante, H., Villasenor-Pineda, L., Villatoro-Tello, E.: INAOE’s participation at PAN’13: Author Profiling task—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Meina, M., Brodzinska, K., Celmer, B., Czokow, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based Classification for Author Profiling using Various Features—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “How Old Do You Think I Am?”; A Study of Language and Age in Twitter. In: Proc. of ICWSM 2013 (2013)Nguyen, D., Smith, N.A., Rosé, C.P.: Author Age Prediction from Text Using Linear Regression. In: Proc. of LaTeCH at ACL-HLTGopal Patra, B., Banerjee, S., Das, D., Saikh, T., Bandyopadhyay, S.: Automatic Author Profiling Based on Linguistic and Stylistic Features—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting Age and Gender in Online Social Networks. In: Proc. of SMUC 2011 (2011)Pennebaker, J.W.: The Secret Life of Pronouns: What Our Words Say About Us. Bloomsbury, USA (2013)Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological Aspects of Natural Language Use: Our Words, Our Selves. Annual Review of Psychology 54(1), 547–577 (2003)Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.: Overview of the 1st International Competition on Plagiarism Detection. In: Proc. of PAN at SEPLN 2009 (2009)Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd International Competition on Plagiarism Detection. In: Proc. of CLEF 2010 (2010)Potthast, M., Stein, B., Barrón-Cedeño, A., Rosso, P.: An Evaluation Framework for Plagiarism Detection. In: Proc. of COLING 2010 (2010)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd International Competition on Plagiarism Detection. In: Proc. of CLEF 2011 (2011)Potthast, M., Gollub, T., Hagen, M., Graßegger, J., Kiesel, J., Michel, M., Oberländer, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th International Competition on Plagiarism Detection. In: Proc. of CLEF 2012 (2012)Potthast, M., Hagen, M., Stein, B., Graßegger, J., Michel, M., Tippmann, M., Welsch, C.: ChatNoir: A Search Engine for the ClueWeb09 Corpus. In: Proc. of SIGIR 2012 (2012)Potthast, M., Gollub, T., Hagen, M., Tippmann, M., Kiesel, J., Rosso, P., Stamatatos, E., Stein, B.: Overview of the 5th International Competition on Plagiarism Detection. In: Proc. of CLEF 2013 (2013)Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing Interaction Logs to Understand Text Reuse from the Web. In: Proc. of ACL 2013. ACM (to appear, August 2013b)Rodíguez Torrejón, D.A., Martín Ramos, J.M.: Text Alignment Module in CoReMo 2.1 Plagiarism Detector—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) 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The Language of Online Child Sexual Groomers - A Corpus Assisted Discourse Study of Intentions, Requests and Grooming Duration

Online grooming has become a wide-spread and worryingly fast increasing issue in society. This thesis analyses a corpus of online grooming communication, made available by the Perverted Justice (PJ) archive, a non-profit organisation that from 2004 until 2019 employed volunteers, who pretended to be children and entered chat rooms to catch and convict groomers, collaborating with law enforcement. The archive consists of 622 grooming chat logs and approx. 3.7 million words of groomer language. A corpus of this database was built, and a Corpus-Assisted Discourse Studies (CADS) approach used to analyse the language therein. Specifically, the language was compared to a reference corpus of general chat language data (PAN2012) and duration of online grooming and manipulative requesting behaviour were also investigated. The following research questions were answered: 1)What are the features of a corpus of online groomer language compared to that of a general digital chat language reference corpus? Is online groomer language distinct? How are online grooming intentions realised linguistically by online groomers?2)Does duration of grooming influence the grooming process/intentions? Is usage of specific words/specific grooming intentions associated with different duration of grooming? Can different duration profiles be established and, if so, what are the cut-off points for these duration profiles?3)How are requests realised in online grooming and how does duration influence this? How do groomers make requests and what support move functions do they use? Does duration influence how requests are made, and the type of support move function that are used?The thesis newly identifies nuanced linguistic realisations of groomers’ intentions and strategies, proposing a new working terminology for discourse-based models of online grooming. This is based on a review of the literature followed by an empirical analysis refining this terminology, which has not been done before. It finds evidence for two distinct duration-based grooming approaches and yields a fine-grained qualitative analysis of groomer requests, also influenced by grooming duration. There have only been very few studies using a CADS analysis of such a large dataset of groomer language and this thesis will lead to new insights, implications and significance for the successful analysis, detection and prevention of online grooming