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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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A Twitter Political Corpus of the 2019 10N Spanish Election
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Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
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Multimodal Fake News Detection with Textual, Visual and Semantic Information
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An Emotional Analysis of False Information in Social Media and News Articles
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Irony Detection in Twitter with Imbalanced Class Distributions
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#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Fine-Grained Analysis of Language Varieties and Demographics
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Abstract:
[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/S1351324920000108 ; S ; 641 ; 661 ; 26 ; 6 ; Kestemont, M. , Tschuggnall, M. , Stamatatos, E. , Daelemans, W. , Specht, G. , Stein, B. and Potthast, M. (2018). Overview of the Author Identification Task at PAN-2018: Cross-domain Authorship Attribution and Style Change Detection. CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org. ; McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157. doi:10.1007/bf02295996 ; Lui, M. and Cook, P. (2013). Classifying english documents by national dialect. In Proceedings of the Australasian Language Technology Association Workshop, Citeseer pp. 5–15. ; Basile, A. , Dwyer, G. , Medvedeva, M. , Rawee, J. , Haagsma, H. and Nissim, M. (2017). Is there life beyond n-grams? A simple SVM-based author profiling system. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds), CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org. ; Elfardy, H. and Diab, M.T. (2013). Sentence level dialect identification in arabic. In Association for Computational Linguistics (ACL), pp. 456–461. ; Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. doi:10.1016/0306-4573(88)90021-0 ; Zaghouani, W. and Charfi, A. (2018a). ArapTweet: A large MultiDialect Twitter corpus for gender, age and language variety identification. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan. ; Zampieri, M. , Tan, L. , Ljubešić, N. , Tiedemann, J. and Nakov, P. (2015). Overview of the DSL shared task 2015. In Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects, pp. 1–9. ; Huang, C.-R. and Lee, L.-H. (2008). Contrastive approach towards text source classification based on top-bag-of-word similarity. In PACLIC, pp. 404–410. ; Zaidan, O. F., & Callison-Burch, C. (2014). Arabic Dialect Identification. Computational Linguistics, 40(1), 171-202. doi:10.1162/coli_a_00169 ; Grouin, C. , Forest, D. , Paroubek, P. and Zweigenbaum, P. (2011). Présentation et résultats du défi fouille de texte DEFT2011 Quand un article de presse a t-il été écrit? À quel article scientifique correspond ce résumé? Actes du septième Défi Fouille de Textes, p. 3. ; Martinc, M. , Skrjanec, I. , Zupan, K. and Pollak, S. Pan (2017). Author profiling – gender and language variety prediction. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds), CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org. ; Rangel, F. , Rosso, P. and Franco-Salvador, M. (2016b). A low dimensionality representation for language variety identification. In 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing, LNCS. Springer-Verlag, arxiv:1705.10754. ; Hagen, M. , Potthast, M. and Stein, B. (2018). Overview of the Author Obfuscation Task at PAN 2018. CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org. ; Zampieri, M. and Gebre, B.G. (2012). Automatic identification of language varieties: The case of portuguese. In The 11th Conference on Natural Language Processing (KONVENS), pp. 233–237 (2012) ; Rangel, F. , Rosso, P. , Montes-y-Gómez, M. , Potthast, M. and Stein, B. (2018). Overview of the 6th Author Profiling Task at PAN 2018: Multimodal Gender Identification in Twitter. In CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org. ; Heitele, D. (1975). An epistemological view on fundamental stochastic ideas. Educational Studies in Mathematics, 6(2), 187-205. doi:10.1007/bf00302543 ; Inches, G. and Crestani, F. (2012). Overview of the International Sexual Predator Identification Competition at PAN-2012. CLEF Online working notes/labs/workshop, vol. 30. ; Rosso, P. , Rangel Pardo, F.M. , Ghanem, B. and Charfi, A. (2018b). 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/w14-5904 ; Franco-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_3 ; Rosso, 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/lnc3.12275 ; Malmasi, 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). Findings of the vardial evaluation campaign 2017. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects, pp. 1–15. ; Bogdanova, D., Rosso, P., & Solorio, T. (2014). Exploring high-level features for detecting cyberpedophilia. Computer Speech & Language, 28(1), 108-120. doi:10.1016/j.csl.2013.04.007 ; Maier, W. and Gómez-Rodríguez, C. (2014). Language Variety Identification in Spanish Tweets. LT4CloseLang. ; Castro, D. , Souza, E. , de Oliveira, A.L.I. (2016). Discriminating between Brazilian and European Portuguese national varieties on Twitter texts. In 5th Brazilian Conference on Intelligent Systems (BRACIS), pp. 265–270. ; Zaghouani, W. and Charfi, A. (2018b). Guidelines and annotation framework for Arabic author profiling. In Proceedings of the 3rd Workshop on Open-Source Arabic Corpora and Processing Tools, 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan. ; Hernández Fusilier, D., Montes-y-Gómez, M., Rosso, P., & Guzmán Cabrera, R. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information Processing & Management, 51(4), 433-443. doi:10.1016/j.ipm.2014.11.001 ; Tellez, E.S. , Miranda-Jiménez, S. , Graff, M. and Moctezuma, D. (2017). Gender and language variety identification with microtc. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds). CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org. ; Kandias, M., Stavrou, V., Bozovic, N., & Gritzalis, D. (2013). Proactive insider threat detection through social media. Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society. doi:10.1145/2517840.2517865
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Keyword:
Age; Arabic; Author profiling; Cybersecurity; Demographics; Gender; Language variety identification; LENGUAJES Y SISTEMAS INFORMATICOS
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URL: http://hdl.handle.net/10251/166834
https://doi.org/10.1017/S1351324920000108
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| 30 |
Multilingual Stance Detection in Social Media Political Debates
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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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