Author Profiling in Social Media: The Impact of Emotions on Discourse Analysis

[EN] In this paper we summarise the content of the keynote that will be given at the 5th International Conference on Statistical Language and Speech Processing (SLSP) in Le Mans, France in October 23¿25, 2017. In the keynote we will address the importance of inferring demographic information for marketing and security reasons. The aim is to model how language is shared in gender and age groups taking into account its statistical usage. We will see how a shallow discourse analysis can be done on the basis of a graph-based representation in order to extract information such as how complicated the discourse is (i.e., how connected the graph is), how much interconnected grammatical categories are, how far a grammatical category is from others, how different grammatical categories are related to each other, how the discourse is modelled in different structural or stylistic units, what are the grammatical categories with the most central use in the discourse of a demographic group, what are the most common connectors in the linguistic structures used, etc. Moreover, we will see also the importance to consider emotions in the shallow discourse analysis and the impact that this has. We carried out some experiments for identifying gender and age, both in Spanish and in English, using PAN-AP-13 and PAN-PC-14 corpora, obtaining comparable results to the best performing systems of the PAN Lab at CLEF.The research work described in this paper was partially carried out in the framework of the SomEMBED project (TIN2015-71147-C2-1-P), funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO).Rosso, P.; Rangel-Pardo, FM. (2017). Author Profiling in Social Media: The Impact of Emotions on Discourse Analysis. Lecture Notes in Computer Science. 10583:3-18. https://doi.org/10.1007/978-3-319-68456-7_1S31810583Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), 10008 (2008)Bonacich, P.: Factoring and weighting approaches to clique identification. J. Math. Soc. 2(1), 113–120 (1972)Brandes, U.: A faster algorithm for betweenness centrality. J. Math. 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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). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass. 12(4):1-20. https://doi.org/10.1111/lnc3.12275S120124Abuhakema , G. Faraj , R. Feldman , A. Fitzpatrick , E. 2008 Annotating an arabic learner corpus for error Proceedings of The sixth international conference on Language Resources and Evaluation, LREC 2008Adouane , W. Dobnik , S. 2017 Identification of languages in algerian arabic multilingual documents Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP)Adouane , W. Semmar , N. Johansson , R 2016a Romanized berber and romanized arabic automatic language identification using machine learning Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 53 61Adouane , W. Semmar , N. Johansson , R. 2016b ASIREM participation at the discriminating similar languages shared task 2016 Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 163 169Adouane , W. Semmar , N. Johansson , R. Bobicev , V. 2016c Automatic detection of arabicized berber and arabic varieties Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 63 72Alfaifi , A. Atwell , E. Hedaya , I. 2014 Arabic learner corpus (ALC) v2: A new written and spoken corpus of Arabic learnersAlharbi , K. 2015 The irony volcano explodes black comedyAli , A. Bell , P. Renals , S. 2015 Automatic dialect detection in Arabic broadcast speechAlmeman , K. Lee , M. 2013 Automatic building of Arabic multi dialect text corpora by bootstrapping dialect words 1 6Aloshban , N. Al-Dossari , H. 2016 A new approach for group spam detection in social media for Arabic language (AGSD) 20 23Al-Sabbagh , R. 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Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification Task

[EN] In this paper, we describe the system proposed by UO-UPV team for addressing the task Profiling Hate Speech Spreaders on Twitter shared at PAN 2021. The system relies on a modular architecture, combining Deep Learning models with an introduced variant of the Impostor Method (IM). It receives a single profile composed of a fixed quantity of tweets. These posts are encoded as dense feature vectors using a fine-tuned transformer model and later combined to represent the whole profile. For classifying a new profile as hate speech spreader or not, it is compared by a similarity function with the Impostor Method with respect to random sampled prototypical profiles. In the final evaluation phase, our model achieved 74% and 82% of accuracy for English and Spanish languages respectively, ranking our team at 2¿¿ position and giving a starting point for further improvements.The work of the third author was in the framework of the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31), funded by Spanish Ministry of Science and Innovation, and DeepPattern (PROMETEO/2019/121), funded by the Generalitat Valenciana.Labadie Tamayo, R.; Castro Castro, D.; Ortega-Bueno, R. (2021). Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification Task. CEUR. 2035-2046. http://hdl.handle.net/10251/1906692035204

Overview of PAN'17: Author Identification, Author Profiling, and Author Obfuscation

[EN] The PAN 2017 shared tasks on digital text forensics were held in conjunction with the annual CLEF conference. This paper gives a high-level overview of each of the three shared tasks organized this year, namely author identification, author profiling, and author obfuscation. For each task, we give a brief summary of the evaluation data, performance measures, and results obtained. Altogether, 29 participants submitted a total of 33 pieces of software for evaluation, whereas 4 participants submitted to more than one task. All submitted software has been deployed to the TIRA evaluation platform, where it remains hosted for reproducibility purposes.The work at the Universitat Politècnica de València was funded by the MINECO research project SomEMBED (TIN2015-71147-C2-1-P).Potthast, M.; Rangel-Pardo, FM.; Tschuggnall, M.; Stamatatos, E.; Rosso, P.; Stein, B. (2017). Overview of PAN'17: Author Identification, Author Profiling, and Author Obfuscation. 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In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Glavaš, G., Nanni, F., Ponzetto, S.P.: Unsupervised text segmentation using semantic relatedness graphs. In: Association for Computational Linguistics (2016)Gollub, T., Stein, B., Burrows, S.: Ousting ivory tower research: towards a web framework for providing experiments as a service. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M. (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), pp. 1125–1126. ACM, August 2012Gómez-Adorno, H., Aleman, Y., no, D.V., Sanchez-Perez, M.A., Pinto, D., Sidorov, G.: Author clustering using hierarchical clustering analysis. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2017: safety evaluation revisited. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Halvani, O., Graner, L.: Author clustering based on compression-based dissimilarity scores. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) CLEF 2017 Working Notes. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Comput. Linguist. 23(1), 33–64 (1997)Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Advances in Neural Information Processing Systems (NIPS), pp. 3294–3302 (2015)Kocher, M., Savoy, J.: UniNE at CLEF 2017: author clustering. 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Overview of the 2nd Author Profiling Task at PAN 2014

[EN] This overview presents the framework and the results for the Author Profiling task at PAN 2014. Objective of this year is the analysis of the adaptability of the detection approaches when given different genres. For this purpose a corpus with four different parts (subcorpora) has been compiled: social media, Twitter, blogs, and hotel reviews. The construction of the Twitter subcorpus happened in cooperation with RepLab in order to investigate also a reputational perspective. Altogether, the approaches of 10 participants are evaluated.The PAN task on author profiling has been organised in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie People Framework of the European Commission. We would like to thank Atribus by Corex for sponsoring the award for the winner team. We thank Julio Gonzalo, Jorge Carrillo and Damiano Spina from UNED for helping with the Twitter subcorpus. The work of the first author was partially funded by Autoritas Consulting SA and by Ministerio de Economía y Competitividad de España under grant ECOPORTUNITY IPT-2012-1220-430000 and CSO2013-43054-R. The work of the second author was in the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Rangel, F.; Rosso, P.; Chrugur, I.; Potthast, M.; Trenkmann, M.; Stein, B.; Verhoeven, B.... (2014). Overview of the 2nd Author Profiling Task at PAN 2014. CEUR Workshop Proceedings. 1180:898-927. http://hdl.handle.net/10251/61150S898927118