A Profile-Based Method for Authorship Verification

Abstract. Authorship verification is one of the most challenging tasks in stylebased text categorization. Given a set of documents, all by the same author, and another document of unknown authorship the question is whether or not the latter is also by that author. Recently, in the framework of the PAN-2013 evaluation lab, a competition in authorship verification was organized and the vast majority of submitted approaches, including the best performing models, followed the instance-based paradigm where each text sample by one author is treated separately. In this paper, we show that the profile-based paradigm (where all samples by one author are treated cumulatively) can be very effective surpassing the performance of PAN-2013 winners without using any information from external sources. The proposed approach is fully-trainable and we demonstrate an appropriate tuning of parameter settings for PAN-2013 corpora achieving accurate answers especially when the cost of false negatives is high.

DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%

A Decade of Shared Tasks in Digital Text Forensics at PAN

[EN] Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, to extract and analyze information about the authors of these documents. The research field has been substantially developed during the last decade. PAN is a series of shared tasks that started in 2009 and significantly contributed to attract the attention of the research community in well-defined digital text forensics tasks. Several benchmark datasets have been developed to assess the state-of-the-art performance in a wide range of tasks. In this paper, we present the evolution of both the examined tasks and the developed datasets during the last decade. We also briefly introduce the upcoming PAN 2019 shared tasks.We are indebted to many colleagues and friends who contributed greatly to PAN's tasks: Maik Anderka, Shlomo Argamon, Alberto Barrón-Cedeño, Fabio Celli, Fabio Crestani, Walter Daelemans, Andreas Eiselt, Tim Gollub, Parth Gupta, Matthias Hagen, Teresa Holfeld, Patrick Juola, Giacomo Inches, Mike Kestemont, Moshe Koppel, Manuel Montes-y-Gómez, Aurelio Lopez-Lopez, Francisco Rangel, Miguel Angel Sánchez-Pérez, Günther Specht, Michael Tschuggnall, and Ben Verhoeven. Our special thanks go to PAN¿s sponsors throughout the years and not least to the hundreds of participants.Potthast, M.; Rosso, P.; Stamatatos, E.; Stein, B. (2019). A Decade of Shared Tasks in Digital Text Forensics at PAN. Lecture Notes in Computer Science. 11438:291-300. https://doi.org/10.1007/978-3-030-15719-7_39S2913001143

Detecting Sockpuppets in Deceptive Opinion Spam

This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.Comment: 18 pages, Accepted at CICLing 2017, 18th International Conference on Intelligent Text Processing and Computational Linguistic

Overview of the PAN/CLEF 2015 Evaluation Lab

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24027-5_49This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of text mining research focusing on the identification of personal traits of authors left behind in texts unintentionally. PAN 2015 comprises three tasks: plagiarism detection, author identification and author profiling studying important variations of these problems. In plagiarism detection, community-driven corpus construction is introduced as a new way of developing evaluation resources with diversity. In author identification, cross-topic and cross-genre author verification (where the texts of known and unknown authorship do not match in topic and/or genre) is introduced. A new corpus was built for this challenging, yet realistic, task covering four languages. In author profiling, in addition to usual author demographics, such as gender and age, five personality traits are introduced (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and a new corpus of Twitter messages covering four languages was developed. In total, 53 teams participated in all three tasks of PAN 2015 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.Stamatatos, E.; Potthast, M.; Rangel, F.; Rosso, P.; Stein, B. (2015). Overview of the PAN/CLEF 2015 Evaluation Lab. En Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings. Springer International Publishing. 518-538. doi:10.1007/978-3-319-24027-5_49S518538Álvarez-Carmona, M.A., López-Monroy, A.P., Montes-Y-Gómez, M., Villaseñor-Pineda, L., Jair-Escalante, H.: INAOE’s participation at PAN 2015: author profiling task–notebook for PAN at CLEF 2015. In: CLEF 2013 Working Notes. CEUR (2015)Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, Genre, and Writing Style in Formal Written Texts. TEXT 23, 321–346 (2003)Bagnall, D.: Author identification using multi-headed recurrent neural networks. In: CLEF 2015 Working Notes. CEUR (2015)Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: Proceedings of EMNLP 2011. ACL (2011)Burrows, S., Potthast, M., Stein, B.: Paraphrase Acquisition via Crowdsourcing and Machine Learning. ACM TIST 4(3), 43:1–43:21 (2013)Castillo, E., Cervantes, O., Vilariño, D., Pinto, D., León, S.: Unsupervised method for the authorship identification task. In: CLEF 2014 Labs and Workshops, Notebook Papers. CEUR (2014)Celli, F., Lepri, B., Biel, J.I., Gatica-Perez, D., Riccardi, G., Pianesi, F.: The workshop on computational personality recognition 2014. In: Proceedings of ACM MM 2014 (2014)Celli, F., Pianesi, F., Stillwell, D., Kosinski, M.: Workshop on computational personality recognition: shared task. In: Proceedings of WCPR at ICWSM 2013 (2013)Celli, F., Polonio, L.: Relationships between personality and interactions in facebook. In: Social Networking: Recent Trends, Emerging Issues and Future Outlook. Nova Science Publishers, Inc. (2013)Chaski, C.E.: Who’s at the Keyboard: Authorship Attribution in Digital Evidence Invesigations. International Journal of Digital Evidence 4 (2005)Chittaranjan, G., Blom, J., Gatica-Perez, D.: Mining Large-scale Smartphone Data for Personality Studies. Personal and Ubiquitous Computing 17(3), 433–450 (2013)Fréry, J., Largeron, C., Juganaru-Mathieu, M.: UJM at clef in author identification. In: CLEF 2014 Labs and Workshops, Notebook Papers. CEUR (2014)Gollub, T., Potthast, M., Beyer, A., Busse, M., Rangel, F., Rosso, P., Stamatatos, E., Stein, B.: Recent trends in digital text forensics and its evaluation. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 282–302. Springer, Heidelberg (2013)Gollub, T., Stein, B., Burrows, S.: Ousting ivory tower research: towards a web framework for providing experiments as a service. In: Proceedings of SIGIR 2012. ACM (2012)Hagen, M., Potthast, M., Stein, B.: Source retrieval for plagiarism detection from large web corpora: recent approaches. In: CLEF 2015 Working Notes. CEUR (2015)van Halteren, H.: Linguistic profiling for author recognition and verification. In: Proceedings of ACL 2004. ACL (2004)Holmes, J., Meyerhoff, M.: The Handbook of Language and Gender. Blackwell Handbooks in Linguistics. Wiley (2003)Jankowska, M., Keselj, V., Milios, E.: CNG text classification for authorship profiling task–notebook for PAN at CLEF 2013. In: CLEF 2013 Working Notes. CEUR (2013)Juola, P.: Authorship Attribution. Foundations and Trends in Information Retrieval 1, 234–334 (2008)Juola, P.: How a Computer Program Helped Reveal J.K. Rowling as Author of A Cuckoo’s Calling. Scientific American (2013)Juola, P., Stamatatos, E.: Overview of the author identification task at PAN-2013. In: CLEF 2013 Working Notes. CEUR (2013)Kalimeri, K., Lepri, B., Pianesi, F.: Going beyond traits: multimodal classification of personality states in the wild. In: Proceedings of ICMI 2013. ACM (2013)Koppel, M., Argamon, S., Shimoni, A.R.: Automatically Categorizing Written Texts by Author Gender. Literary and Linguistic Computing 17(4) (2002)Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring Differentiability: Unmasking Pseudonymous Authors. J. Mach. Learn. Res. 8, 1261–1276 (2007)Koppel, M., Winter, Y.: Determining if Two Documents are Written by the same Author. Journal of the American Society for Information Science and Technology 65(1), 178–187 (2014)Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., Graepel, T.: Manifestations of User Personality in Website Choice and Behaviour on Online Social Networks. Machine Learning (2013)López-Monroy, A.P., y Gómez, M.M., Jair-Escalante, H., Villaseñor-Pineda, L.: Using intra-profile information for author profiling–notebook for PAN at CLEF 2014. In: CLEF 2014 Working Notes. CEUR (2014)Lopez-Monroy, A.P., Montes-Y-Gomez, M., Escalante, H.J., Villasenor-Pineda, L., Villatoro-Tello, E.: INAOE’s participation at PAN 2013: author profiling task-notebook for PAN at CLEF 2013. In: CLEF 2013 Working Notes. CEUR (2013)Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of COLING 2008 (2008)Maharjan, S., Shrestha, P., Solorio, T., Hasan, R.: A straightforward author profiling approach in mapreduce. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS, vol. 8864, pp. 95–107. Springer, Heidelberg (2014)Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research 30(1), 457–500 (2007)Eissen, S.M., Stein, B.: Intrinsic plagiarism detection. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 565–569. Springer, Heidelberg (2006)Mohammadi, G., Vinciarelli, A.: Automatic personality perception: Prediction of Trait Attribution Based on Prosodic Features. IEEE Transactions on Affective Computing 3(3), 273–284 (2012)Moreau, E., Jayapal, A., Lynch, G., Vogel, C.: Author verification: basic stacked generalization applied to predictions from a set of heterogeneous learners. In: CLEF 2015 Working Notes. CEUR (2015)Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “How old do you think I am?”; a study of language and age in twitter. In: Proceedings of ICWSM 2013. AAAI (2013)Oberlander, J., Nowson, S.: Whose thumb is it anyway?: classifying author personality from weblog text. In: Proceedings of COLING 2006. ACL (2006)Peñas, A., Rodrigo, A.: A simple measure to assess non-response. In: Proceedings of HLT 2011. ACL (2011)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., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd international competition on plagiarism detection. In: CLEF 2010 Working Notes. CEUR (2010)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-Language Plagiarism Detection. Language Resources and Evaluation (LRE) 45, 45–62 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: CLEF 2011 Working Notes (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: CLEF 2012 Working Notes. CEUR (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: CLEF 2013 Working Notes. CEUR (2013)Potthast, M., Gollub, T., Rangel, F., Rosso, P., Stamatatos, E., Stein, B.: Improving the reproducibility of PAN’s shared tasks: plagiarism detection, author identification, and author profiling. In: Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., Toms, E. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 268–299. Springer, Heidelberg (2014)Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th international competition on plagiarism detection. In: CLEF 2014 Working Notes. CEUR (2014)Potthast, M., Göring, S., Rosso, P., Stein, B.: Towards data submissions for shared tasks: first experiences for the task of text alignment. In: CLEF 2015 Working Notes. CEUR (2015)Potthast, M., Hagen, M., Stein, B., Graßegger, J., Michel, M., Tippmann, M., Welsch, C.: ChatNoir: a search engine for the clueweb09 corpus. In: Proceedings of SIGIR 2012. ACM (2012)Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing interaction logs to understand text reuse from the web. In: Proceedings of ACL 2013. ACL (2013)Potthast, M., Stein, B., Barrón-Cedeño, A., Rosso, P.: An evaluation framework for plagiarism detection. In: Proceedings of COLING 2010. ACL (2010)Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.: Overview of the 1st international competition on plagiarism detection. In: Proceedings of PAN at SEPLN 2009. CEUR (2009)Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., Crowcroft, J.: The personality of popular facebook users. In: Proceedings of CSCW 2012. ACM (2012)Rammstedt, B., John, O.: Measuring Personality in One Minute or Less: A 10 Item Short Version of the Big Five Inventory in English and German. Journal of Research in Personality (2007)Rangel, F., Rosso, P.: On the impact of emotions on author profiling. In: Information Processing & Management, Special Issue on Emotion and Sentiment in Social and Expressive Media (2014) (in press)Rangel, F., Rosso, P., Celli, F., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: CLEF 2015 Working Notes. CEUR (2015)Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkmann, M., Stein, B., Verhoeven, B., Daelemans, W.: Overview of the 2nd author profiling task at PAN 2014. In: CLEF 2014 Working Notes. CEUR (2014)Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013–notebook for PAN at CLEF 2013. In: CLEF 2013 Working Notes. CEUR (2013)Sapkota, U., Bethard, S., Montes-y-Gómez, M., Solorio, T.: Not all character N-grams are created equal: a study in authorship attribution. In: Proceedings of NAACL 2015. ACL (2015)Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S., Rosso, P.: Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of COLING 2014 (2014)Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. AAAI (2006)Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E., et al.: Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PloS one 8(9), 773–791 (2013)Stamatatos, E.: A Survey of Modern Authorship Attribution Methods. Journal of the American Society for Information Science and Technology 60, 538–556 (2009)Stamatatos, E.: On the Robustness of Authorship Attribution Based on Character N-gram Features. Journal of Law and Policy 21, 421–439 (2013)Stamatatos, E., Daelemans, W., Verhoeven, B., Juola, P., López-López, A., Potthast, M., Stein, B.: Overview of the author identification task at PAN 2015. In: Working Notes Papers of the CLEF 2015 Evaluation Labs. CEUR (2015)Stamatatos, E., Daelemans, W., Verhoeven, B., Stein, B., Potthast, M., Juola, P., Sánchez-Pérez, M.A., Barrón-Cedeño, A.: Overview of the author identification task at PAN 2014. In: CLEF 2014 Working Notes. CEUR (2014)Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic Text Categorization in Terms of Genre and Author. Comput. Linguist. 26(4), 471–495 (2000)Stein, B., Lipka, N., Prettenhofer, P.: Intrinsic Plagiarism Analysis. Language Resources and Evaluation (LRE) 45, 63–82 (2011)Stein, B., Meyer zu Eißen, S.: Near similarity search and plagiarism analysis. In: Proceedings of GFKL 2005. Springer (2006)Sushant, S.A., Argamon, S., Dhawle, S., Pennebaker, J.W.: Lexical predictors of personality type. In: Proceedings of Joint Interface/CSNA 2005Verhoeven, B., Daelemans, W.: Clips stylometry investigation (CSI) corpus: a dutch corpus for the detection of age, gender, personality, sentiment and deception in text. In: Proceedings of LREC 2014. ACL (2014)Weren, E., Kauer, A., Mizusaki, L., Moreira, V., de Oliveira, P., Wives, L.: Examining Multiple Features for Author Profiling. Journal of Information and Data Management (2014)Zhang, C., Zhang, P.: Predicting gender from blog posts. Tech. rep., Technical Report. University of Massachusetts Amherst, USA (2010

Quantum Phase Interference and Neel-Vector Tunneling in Antiferromagnetic Molecular Wheels

The antiferromagnetic molecular wheel Fe18 of eighteen exchange-coupled Fe(III) ions has been studied by measurements of the magnetic torque, the magnetization, and the inelastic neutron scattering spectra. The combined data show that the low-temperature magnetism of Fe18 is very accurately described by the Neel-vector tunneling (NVT) scenario, as unfolded by semiclassical theory. In addition, the magnetic torque as a function of applied field exhibits oscillations that reflect the oscillations in the NVT tunnel splitting with field due to quantum phase interference.Comment: 5 pages, 4 figures, REVTEX4, to appear in PR

Enhancement of human immunodeficiency virus type 1 infection by cationic liposomes: The role of CD4, serum and liposome-cell interactions

We have reported previously the enhancement of the infectivity of human immunodeficiency virus type 1 (HIV- 1) by liposomes composed of the cationic lipid N[2,3-(dioleyloxy) propyl]-N,N,N-trimethylammonium chloride (DOTMA). To determine the mechanism by which this process occurs, we have investigated the role of CD4, serum concentration and liposome-cell interactions in the DOTMA-mediated stimulation of HIV-1 infection of A3.01 cells. Serum alone significantly inhibited the binding and infectivity of HIV-1, but DOTMA-mediated enhancement of infectivity was more pronounced in the presence of serum than in its absence. HIV-1 binding to cells was increased in the presence of DOTMA liposomes, DEAE-dextran and polybrene, all of which also enhanced infectivity to a similar extent at comparable concentrations. Fluorescence dequenching measurements indicated that DOTMA liposomes fused with HIV-1, but not with cell membranes, in the presence of serum. The enhancing effect of DOTMA liposomes on HIV-1 infectivity was CD4-dependent, and appeared to involve virus-liposome fusion and liposome binding to the cell surface. DOTMA liposomes did not mediate infection of the CD4- K562 and Raji cell lines

Automatic extraction of rules for sentence boundary disambiguation

ABSTRACT Transformation-based learning (TBL) is the most important machine learning theory aiming at the automatic extraction of rules based on already tagged corpora. However, the application of this theory to a certain application without taking into account the features that characterize this application may cause problems regarding the training time cost as well as the accuracy of the extracted rules. In this paper we present a variation of the basic idea of the TBL and we apply it to the extraction of the sentence boundary disambiguation rules in real-world text, a prerequisite for the vast majority of the natural language processing applications. We show that our approach achieves considerably higher accuracy results and, moreover, requires minimal training time in comparison to the traditional TBL

The use of orthogonal similarity relations in the prediction of authorship

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37256-8_38Recent work on Authorship Attribution (AA) proposes the use of meta characteristics to train author models. The meta characteristics are orthogonal sets of similarity relations between the features from the different candidate authors. In that approach, the features are grouped and processed separately according to the type of information they encode, the so called linguistic modalities. For instance, the syntactic, stylistic and semantic features are each considered different modalities as they represent different aspects of the texts. The assumption is that the independent extraction of meta characteristics results in more informative feature vectors, that in turn result in higher accuracies. In this paper we set out to the task of studying the empirical value of this modality specific process. We experimented with different ways of generating the meta characteristics on different data sets with different numbers of authors and genres. Our results show that by extracting the meta characteristics from splitting features by their linguistic dimension we achieve consistent improvement of prediction accuracy.This research was partially supported by ONR grant N00014-12-1-0217 and by NSF award 1254108. It was also supported in part by the CONACYT grant 134186 and by the European Commission as part of the WIQ-EI project (project no. 269180) within the FP7 People Programme.Sapkota, U.; Solorio, T.; Montes Gómez, M.; Rosso, P. (2013). The use of orthogonal similarity relations in the prediction of authorship. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 463-475. https://doi.org/10.1007/978-3-642-37256-8_38S463475Baker, L.D., McCallum, A.: Distributional clustering of words for text classification. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR, pp. 96–103. ACM, Melbourne (1998)Biber, D.: The multi-dimensional approach to linguistic analyses of genre variation: An overview of methodology and findings. Computers and the Humanities 26, 331–345 (1993)Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 1998 Conference on Computational Learning Theory (1998)Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information-theoretic feature clsutering algorithm for text classification. Journal of Machine Learning Research 3, 1265–1287 (2003)Escalante, H.J., Montes-y-Gómez, M., Solorio, T.: A weighted profile intersection measure for profile-based authorship attribution. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 232–243. Springer, Heidelberg (2011)Escalante, H.J., Solorio, T., Montes-y-Gomez, M.: Local histograms of character n-grams for authorship attribution. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 288–298. Association for Computational Linguistics, Portland (2011)Hayes, J.H.: Authorship attribution: A principal component and linear discriminant analysis of the consistent programmer hypothesis. I. J. Comput. Appl., 79–99 (2008)Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 77–86. Springer, Heidelberg (2006)Karypis, G.: CLUTO - a clustering toolkit. Tech. Rep. #02-017 (November 2003)Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram based author profiles for authorship attribution. In: Proceedings of the Pacific Association for Computational Linguistics, pp. 255–264 (2003)Koppel, M., Schler, J., Argamon, S.: Authorship attribution in the wild. Language Resources and Evaluation 45, 83–94 (2011)Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, pp. 513–520 (August 2008)Luyckx, K., Daelemans, W.: The effect of author set size and data size in authorship attribution. In: Literary and Linguistic Computing, pp. 1–21 (August 2010)Marneffe, M.D., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: LREC 2006 (2006)Plakias, S., Stamatatos, E.: Tensor space models for authorship identification. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 239–249. Springer, Heidelberg (2008)Raghavan, S., Kovashka, A., Mooney, R.: Authorship attribution using probabilistic context-free grammars. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 38–42. Association for Computational Linguistics, Uppsala (2010)Slonim, N., Tishby, N.: The power of word clusters for text classification. In: 23rd European Colloquium on Information Retrieval Research, ECIR (2001)Solorio, T., Pillay, S., Raghavan, S., Montes-y-Gómez: Generating metafeatures for authorship attribution on web forum posts. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 156–164. AFNLP, Chiang Mai (2011)Stamatatos, E.: Author identification using imbalanced and limited training texts. In: 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, pp. 237–241 (September 2007)Stamatatos, E.: Author identification: Using text sampling to handle the class imbalance problem. Information Processing and Managemement 44, 790–799 (2008)Stamatatos, E.: Plagiarism detection using stopword n-grams. Journal of the American Society for Information Science and Technology 62(12), 2512–2527 (2011)Stamatatos, E.: A survey on modern authorship attribution methods. Journal of the American Society for Information Science and Technology 60(3), 538–556 (2009)Stolcke, A.: SRILM - an extensible language modeling toolkit, pp. 901–904 (2002)Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, vol. 1, pp. 173–180 (2003)de Vel, O., Anderson, A., Corney, M., Mohay, G.: Multi-topic e-mail authorship attribution forensics. In: Proceedings of the Workshop on Data Mining for Security Applications, 8th ACM Conference on Computer Security (2001)Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005