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. 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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. 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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? 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What demographic attributes do our digital footprints reveal? A systematic review

<div><p>To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.</p></div

Improving the Reproducibility of PAN s Shared Tasks

This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling. To improve the reproducibility of shared tasks in general, and PAN’s tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.This work was partially supported by the WIQ-EI IRSESproject (Grant No. 269180) within the FP7 Marie Curie action.Potthast, M.; Gollub, T.; Rangel, F.; Rosso, P.; Stamatatos, E.; Stein, B. (2014). Improving the Reproducibility of PAN s Shared Tasks. En Information Access Evaluation. Multilinguality, Multimodality, and Interaction: 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings. Springer Verlag (Germany). 268-299. https://doi.org/10.1007/978-3-319-11382-1_22S26829

A Spanish text corpus for the author profiling task

Author Profiling is the task of predicting characteristics of the author of a text, such as age, gender, personality, native language, etc. This is a task of growing importance due to its potential applications in security, crime and marketing, among others. One of the main difficulties in this field is the lack of reliable text collections (corpora) to train and test automatically derived classifiers, in particular in specific languages such as Spanish. Although some recent data sets were generated for the PAN competitions, these documents have a lot of “noise” that prevent researchers from obtaining more general conclusions about this task when more formal documents are used. In this context, this work proposes and describes SpanText, a data collection of formal texts in Spanish language which is, as far as we know, the first collection with these characteristics for the author profiling task. Besides, an experimental study is carried out where the difference in performance obtained with formal and informal texts is clearly established and opens interesting research lines to get a deeper understanding of the particularities that each type of documents poses to the author profiling task.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI

A Spanish text corpus for the author profiling task

Author Profiling is the task of predicting characteristics of the author of a text, such as age, gender, personality, native language, etc. This is a task of growing importance due to its potential applications in security, crime and marketing, among others. One of the main difficulties in this field is the lack of reliable text collections (corpora) to train and test automatically derived classifiers, in particular in specific languages such as Spanish. Although some recent data sets were generated for the PAN competitions, these documents have a lot of “noise” that prevent researchers from obtaining more general conclusions about this task when more formal documents are used. In this context, this work proposes and describes SpanText, a data collection of formal texts in Spanish language which is, as far as we know, the first collection with these characteristics for the author profiling task. Besides, an experimental study is carried out where the difference in performance obtained with formal and informal texts is clearly established and opens interesting research lines to get a deeper understanding of the particularities that each type of documents poses to the author profiling task.XI Workshop Bases de Datos y Minería de DatosRed de Universidades con Carreras de Informática (RedUNCI

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

Paraphrase Plagiarism Identifcation with Character-level Features

[EN] Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identi cation consists in automatically recognizing document fragments that contain re-used text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes, and morphological or lexical substitutions. Our main hypothesis establishes that the original author's writing style ngerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identi cation, which represents a new valuable resource to the NLP community for future research work in this field.This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030).Sánchez-Vega, F.; Villatoro-Tello, E.; Montes-Y-Gómez, M.; Rosso, P.; Stamatatos, E.; Villaseñor-Pineda, L. (2019). Paraphrase Plagiarism Identifcation with Character-level Features. Pattern Analysis and Applications. 22(2):669-681. https://doi.org/10.1007/s10044-017-0674-zS66968122