Overview of the PAN'2016 - New Challenges for Authorship Analysis: Cross-genre Profiling, Clustering, Diarization, and Obfuscation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-44564-9_28This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of digital text forensic research. PAN 2016 comprises three shared tasks: (i) author identification, addressing author clustering and diarization (or intrinsic plagiarism detection); (ii) author profiling, addressing age and gender prediction from a cross-genre perspective; and (iii) author obfuscation, addressing author masking and obfuscation evaluation. In total, 35 teams participated in all three shared tasks of PAN 2016 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.The work of the first author was partially supported by the Som EMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMA MATER (Prometeo II/2014/030). The work of the second author was partially supported by Autoritas Consulting and by Ministerio de Economía y Competitividad de España under grant ECOPORTUNITY IPT-2012-1220-430000.Rosso, P.; Rangel-Pardo, FM.; Potthast, M.; Stamatatos, E.; Tschuggnall, M.; Stein, B. (2016). Overview of the PAN'2016 - New Challenges for Authorship Analysis: Cross-genre Profiling, Clustering, Diarization, and Obfuscation. En Experimental IR Meets Multilinguality, Multimodality, and Interaction. Springer Verlag (Germany). 332-350. https://doi.org/10.1007/978-3-319-44564-9_28S332350Almishari, M., Tsudik, G.: Exploring linkability of user reviews. In: Foresti, S., Yung, M., Martinelli, F. (eds.) ESORICS 2012. LNCS, vol. 7459, pp. 307–324. Springer, Heidelberg (2012)Álvarez-Carmona, M.A., López-Monroy, A.P., Montes-Y-Gómez, M., Villaseñor-Pineda, L., Jair-Escalante, H.: INAOE’s Participation at PAN’15: author profiling task–notebook for PAN at CLEF 2015. 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A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). 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Rosso , P. 2015 On the multilingual and genre robustness of emographs for author profiling in social media 274 280 Springer-Verlag, LNCSRangel, F., & Rosso, P. (2016). On the impact of emotions on author profiling. Information Processing & Management, 52(1), 73-92. doi:10.1016/j.ipm.2015.06.003Rangel , F. Rosso , P. Koppel , M. Stamatatos , E. Inches , G. 2013 Overview of the author profiling task at PAN 2013 P. Forner R. Navigli D. TufisRangel , F. Rosso , P. Potthast , M. Stein , B. Daelemans , W. 2015 Overview of the 3rd author profiling task at PAN 2015 L. Cappellato N. Ferro G. Jones E. San JuanRangel , F. Rosso , P. Verhoeven , B. Daelemans , W. Potthast , M. Stein , B. 2016 Overview of the 4th author profiling task at PAN 2016: Cross-genre evaluationsRefaee , E. Rieser , V. 2014 An Arabic twitter corpus for subjectivity and sentiment analysis 2268 2273Reyes, A., Rosso, P., & Buscaldi, D. (2012). From humor recognition to irony detection: The figurative language of social media. 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Predicting Author Traits Through Topic Modeling of Multilingual Social Media Text

One source of insight into the motivations of a modern human being is the text they write and post for public consumption online, in forms such as personal status updates, product reviews, or forum discussions. The task of inferring traits about an author based on their writing is often called "author profiling." One challenging aspect of author profiling in today’s world is the increasing diversity of natural languages represented on social media websites. Furthermore, the informal nature of such writing often inspires modifications to standard spelling and grammatical structure which are highly language-specific. These are some of the dilemmas that inspired a series of "shared task" competitions, in which many participants work to solve a single problem in different ways, in order to compare their methods and results. This thesis describes our submission to one author profiling shared task in which 22 teams implemented software to predict the age, gender, and certain personality traits of Twitter users based on the content of their posts to the website. We will also analyze the performance and implementation of our system compared to those of other teams, all of which were described in open-access reports. The competition organizers provided a labeled training dataset of tweets in English, Spanish, Dutch, and Italian, and evaluated the submitted software on a similar but hidden dataset. Our approach is based on applying a topic modeling algorithm to an auxiliary, unlabeled but larger collection of tweets we collected in each language, and representing tweets from the competition dataset in terms of a vector of 100 topics. We then trained a random forest classifier based on the labeled training dataset to predict the age, gender and personality traits for authors of tweets in the test set. Our software ranked in the top half of participants in English and Italian, and the top third in Dutch

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

Author Profiling and Plagiarism Detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25485-2_6In this chapter we introduce the topics that we will cover in the RuSSIR 2014 course on Author Profiling and Plagiarism Detection (APPD). Author profiling distinguishes between classes of authors studying how language is shared by classes of people. This task helps in identifying profiling aspects such as gender, age, native language, or even personality type. In case of the plagiarism detection task we are not interested in studying how language is shared. On the contrary, given a document we are interested in investigating if the writing style changes in order to unveil text inconsistencies, i.e., unexpected irregularities through the document such as changes in vocabulary, style and text complexity. In fact, when it is not possible to retrieve the source document(s) where plagiarism has been committed from, the intrinsic analysis of the suspicious document is the only way to find evidence of plagiarism. The difficulty in retrieving the source of plagiarism could be due to the fact that the documents are not available on the web or the plagiarised text fragments were obfuscated via paraphrasing or translation (in case the source document was in another language). In this overview, we also discuss the results of the shared tasks on author profiling (gender and age identification) and plagiarism detection that we help to organise at the PAN Lab on Uncovering Plagiarism, Authorship, and Social Software Misuse.The PAN shared tasks on author profil-ing and on plagiarism detection have been organised in the framework of the WIQ-EIIRSES project (Grant No. 269180) within the EC FP 7 Marie Curie People. The research work described in the paper was carried out in the framework of the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction inIntelligent Systems.Rosso, P. (2015). Author Profiling and Plagiarism Detection. En Information Retrieval. Springer. 229-250. https://doi.org/10.1007/978-3-319-25485-2_6S229250Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. TEXT 23, 321–346 (2003)Association of Teachers and Lecturers. School work plagued by plagiarism - ATL survey. Technical report, Association of Teachers and Lecturers, London, UK (2008). (Press release)Barrón-Cedeño, A.: On the mono- and cross-language detection of text re-use and plagiarism. Ph.D. thesis, Universitat Politènica de València (2012)Barrón-Cedeño, A., Rosso, P., Pinto, D., Juan, A.: On cross-lingual plagiarism analysis using a statistical model. 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In: CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613–0073 (2014). http://ceur-ws.org/Vol-1180/Comas, R., Sureda, J., Nava, C., Serrano, L.: Academic cyberplagiarism: a descriptive and comparative analysis of the prevalence amongst the undergraduate students at Tecmilenio University (Mexico) and Balearic Islands University (Spain). In: Proceedings of the International Conference on Education and New Learning Technologies (EDULEARN 2010), Barcelona (2010)Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221–233 (1948)Flores, E., Barrón-Cedeño, A., Rosso, P., Moreno, L.: Desocore: detecting source code re-use across programming languages. In: Proceedings of 12th International Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-2012, pp. 1–4, Montreal, Canada (2012)Flores, E., Barrón-Cedeño, A., Moreno, L., Rosso, P.: Uncovering source code re-use in large-scale programming environments. 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Perfilado automático de usuarios en corpus sociales sobre el movimiento Black Lives Matter

[Resumen]: Con el objetivo de proporcionar una herramienta útil basada en el perfilado automático de autores, a lo largo de este trabajo se profundizará más en este campo en el contexto de las redes sociales, analizando cuáles son los mejores algoritmos, las técnicas más utilizadas y las características más comúnes que se suelen emplear. Asimismo, se desarrollará, bajo la metodología Scrum, una aplicación web en la que se permita a los usuarios perfilar los autores de una colección de textos propia —haciendo uso de algoritmos seleccionados en la fase previa de investigación— y mostrando los resultado del perfilado en un dashboard intuitivo y accesible. Finalmente, para mostrar un caso de uso real de la aplicación, se empleará la colección de referencia sobre el movimiento Black Lives Matter (#BLM) puesta a disposición para este TFG por los tutores del mismo, con el objetivo de analizar el perfil de los usuarios que participaron en los debates en las redes sociales sobre este movimiento de gran relevancia social.[Abstract]: To provide a useful tool based on automatic author profiling, throughout this work, we will research deeper into this field in the context of social networks, analyzing the best algorithms, the most commonly used techniques and the typical features that are often employed. Furthermore, under the Scrum methodology, we will develop a web application that allows users to profile authors of their own text datasets —employing algorithms selected in the previous research phase— and displaying the profiling results on an intuitive and accessible dashboard. Finally, to illustrate a real use case of the application, the reference collection related to the ”Black Lives Matter” movement (#BLM) will be utilized. This collection has been made available for this Bachelor’s Thesis by its supervisors, with the aim of analyzing the profiles of users who participated in social media discussions about this socially significant movement.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202