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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Un ejemplo de cooperación de área vasta. La experiencia y las perspectivas de desarrollo en la Eurorregión Adriática

El artículo analiza el caso de estudio de la Eurorregión Adriática (EA) para ejemplificar la emergencia de la cooperación de área vasta. Este modelo se considera el último desafío de la cooperación transnacional en Europa, puesto que se requieren motivaciones sólidas para cooperar, y la dimensión y el número de participantes conlleva problemas. Empezaremos introduciendo las características y las perspectivas de la EA, luego, entraremos en el core business, destacando tres puntos principales de él: el lobbying de los presidentes, la articulación operacional y la programación estratégica. En el texto, se resalta el papel del mar Adriático como factor de ventaja absoluta para la cooperación, y se termina destacando las condiciones (necesarias pero no suficientes) para la sostenibilidad de dicha cooperación a largo plazo.L'article analitza el cas d'estudi de l'Euroregió Adriàtica (EA), amb l'objectiu d'exemplificar l'emergència de la cooperació d'àrea àmplia. Aquest model és considerat el darrer desafiament de la cooperació transnacional a Europa, atès que es demanen motivacions sòlides per cooperar, i la dimensió i el nombre de participants comporta problemes. Començarem introduint-hi les característiques i les perspectives de l'EA, després, entrarem al core business destacant-ne tres aspectes principals: el lobbying dels presidents, l'articulació operacional i la programació estratègica. Al text, s'hi subratlla el paper de la mar Adriàtica com a factor d'avantatge absolut per a la cooperació, i es clou destacant les condicions (necessàries però no suficients) per a la sostenibilitat d'aquesta cooperació a llarg termini.L'article analyse le cas d'étude de l'Eurorégion Adriatique (EA) pour illustrer l'urgence de la «coopération de zone vaste». Ce modèle de coopération est considéré le dernier défi de la coopération transnationale en Europe: on exige des motivations solides pour coopérer et la dimension et le nombre de participants entraînent des problèmes. Nous commencerons à introduire les caractéristiques et les perspectives de l'EA, ensuite nous entrerons dans le coeur business en soulignant trois points principaux: le lobbying des Présidents, l'articulation opérationnelle et la programmation stratégique. On remarque le rôle de la mer Adriatique comme facteur d'avantage absolu pour la coopération et on termine en soulignant les conditions (nécessaires mais non suffisantes) pour le soutien à long terme de cette coopération.The article analyzes the case study of the Adriatic Euroregion (AE) to exemplify the emergence of «vast area cooperation». This kind of cooperation is considered to be the last challenge of transnational cooperation in Europe; solid motivations are needed to cooperate, and the dimension and number of participants is a source of problems. We will start by introducing the characteristics and perspectives of the AE, and then we will enter the core business, emphasizing three main points: the lobbying of Presidents, operational articulation and strategic programming. The role of the Adriatic Sea as a factor of absolute advantage for cooperation is highlighted. We conclude by emphasizing the conditions (necessary but not sufficient) for long-term sustainability of the above-mentioned cooperation

Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation

Electronic versíon of an article published as International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 25, 2, 2017, 151-174. DOI:10.1142/S0218488517400165 © copyright World Scientific Publishing Company. https://www.worldscientific.com/worldscinet/ijufks[EN] Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation.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.Cagnina, L.; Rosso, P. (2017). Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 25(2):151-174. https://doi.org/10.1142/S0218488517400165S151174252Cambria, E. (2016). Affective Computing and Sentiment Analysis. IEEE Intelligent Systems, 31(2), 102-107. doi:10.1109/mis.2016.31Cambria, E., & Hussain, A. (2015). Sentic Computing. Cognitive Computation, 7(2), 183-185. doi:10.1007/s12559-015-9325-0Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10-18. doi:10.1145/1656274.1656278Hancock, J. T., Curry, L. E., Goorha, S., & Woodworth, M. (2007). On Lying and Being Lied To: A Linguistic Analysis of Deception in Computer-Mediated Communication. Discourse Processes, 45(1), 1-23. doi:10.1080/01638530701739181Hernández 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.2014.11.001Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1), 50-60. doi:10.1214/aoms/1177730491MONTAÑÉS, E., QUEVEDO, J. R., COMBARRO, E. F., DÍAZ, I., & RANILLA, J. (2007). A HYBRID FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(02), 133-151. doi:10.1142/s0218488507004492Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying Words: Predicting Deception from Linguistic Styles. Personality and Social Psychology Bulletin, 29(5), 665-675. doi:10.1177/0146167203029005010Raudys, S. J., & Jain, A. K. (1991). Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(3), 252-264. doi:10.1109/34.75512Wang, G., Xie, S., Liu, B., & Yu, P. S. (2012). Identify Online Store Review Spammers via Social Review Graph. ACM Transactions on Intelligent Systems and Technology, 3(4), 1-21. doi:10.1145/2337542.2337546Webb, G. I. (2000). Machine Learning, 40(2), 159-196. doi:10.1023/a:100765951484

On the multilingual and genre robustness of EmoGraphs for author profiling in social media

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24027-5_28Author profiling aims at identifying different traits such as age and gender of an author on the basis of her writings. We propose the novel EmoGraph graph-based approach where morphosyntactic categories are enriched with semantic and affective information. In this work we focus on testing the robustness of EmoGraphs when applied to age and gender identification. Results with PAN-AP-14 corpus show the competitiveness of the representation over genres and languages. Finally, some interesting insights are shown, for example with topic and emotion bounded genres such as hotel reviews.The research has been carried out in the framework of the European Commission WIQ-EI IRSES (no. 269180) and DIANA - Finding Hidden Knowledge in Texts (TIN2012-38603-C02) projects. The work of the first author was partially funded by Autoritas Consulting SA and by Spanish Ministry of Economics under grant ECOPORTUNITY IPT-2012-1220-430000.Rangel, F.; Rosso, P. (2015). On the multilingual and genre robustness of EmoGraphs for author profiling in social media. 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. 274-280. https://doi.org/10.1007/978-3-319-24027-5_28S274280Argamon, S., Koppel, M., Fine, J., Shimoni, A.: Gender, genre, and writing style informal written texts. TEXT 23, 321–346 (2003)Levin, B.: English Verb Classes and Alternations. University of Chicago Press, Chicago (1993)Mohammad, S.M., Yang, T.: Tracking sentiment in mail: how gender differ on emotional axes. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (2011)Pennebaker, J.W.: The Secret Life of Pronouns: What Our Words Say About Us. Bloomsbury Press (2011)Rangel, F., Rosso, P.: On the impact of emotions on author profiling. Information Processing & Management, Special Issue on Emotion and Sentiment in Social and Expressive Media (in press, 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: Cappellato L., Ferro N., Halvey M., Kraaij, W. (eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180 (2014)Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at pan 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179 (2013)Sidorov, G., Miranda-Jimnez, S., Viveros-Jimnez, F., Gelbukh, F., Castro-Snchez, N., Velsquez, F., Daz-Rangel, I., Surez-Guerra, S., Trevio, A., Gordon-Miranda, J.: Empirical study of opinion mining in spanish tweets. In: 11th Mexican International Conference on Artificial Intelligence, MICAI, pp. 1–4 (2012)Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon (2004

Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction

[EN] This paper summarizes the contributions of the PRHLT- UPV team as a participant in the eRisk 2020 tasks on self-harm detection and prediction of depression levels from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, based on their online activity.We use multi-dimensional representations of language, and compare various deep learning models' performance, exploring rarely approached avenues in previous research, including hierarchical deep learning architectures and pre-trained transformers and language models.The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Rosso, P. (2020). Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction. CEUR Workshop Proceedings. 2696:1-12. http://hdl.handle.net/10251/166536S112269

On the difficulty of automatically detecting irony: beyond a simple case of negation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-013-0652-8It is well known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In the absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be.The research work of Paolo Rosso was done in the framework of the European Commission WIQ-EI Web Information Quality Evaluation Initiative (IRSES grant no. 269180) project within the FP 7 Marie Curie People, 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.Reyes Pérez, A.; Rosso, P. (2014). On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowledge and Information Systems. 40(3):595-614. https://doi.org/10.1007/s10115-013-0652-8S595614403Artstein R, Poesio M (2008) Inter-coder agreement for computational linguistics. Comput Linguistics 34(4):555–596Atserias J, Casas B, Comelles E, González M, Padró L, Padró M (2006) Freeling 1.3: syntactic and semantic services in an open-source nlp library. 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ACM, Hong Kong, China, pp 53–56Clark H, Gerrig R (1984) On the pretense theory of irony. J Exp Psychol Gen 113(1):121–126Colston H (2007) On necessary conditions for verbal irony comprehension. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 97–134Colston H, Gibbs R (2007) A brief history of irony. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 3–24Curcó C (2007) Irony: negation, echo, and metarepresentation. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 269–296Davidov D, Tsur O, Rappoport A (2010) Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the 14th conference on computational natural language learning, CoNLL ’10. 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Academic Press, New York, pp 41–58Horn L, Kato Y (2000) Introduction: negation and polarity at the millennium. In: Horn L, Kato Y (eds) Studies in negation and polarity. Oxford University Press, Oxford, pp 1–19Kaup B, Lüdtke J, Zwaan R (2006) Processing negated sentences with contradictory predicates: is a door that is not open mentally closed? J Pragmat 38:1033–1050Kisilevich S, Ang CS, Last M (2011) Large-scale analysis of self-disclosure patterns among online social networks users: A Russian context. Knowl Inf Syst. doi: 10.1007/s10115-011-0443-zKreuz R (2001) Using figurative language to increase advertising effectiveness. In: Office of Naval Research Military Personnel Research Science Workshop. University of Memphis, Memphis, TNKumon-Nakamura S, Glucksberg S, Brown M (2007) How about another piece of pie: the allusional pretense theory of discourse irony. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, LondonLangacker R (1991) Concept, image and symbol, the cognitive basis of grammar. Mounton de Gruyter, BerlinLiu J, Wang K (2012) Anonymizing bag-valued sparse data by semantic similarity-based clustering. Knowl Inf Syst. doi: 10.1007/s10115-012-0515-8Lucariello J (2007) Situational irony: a concept of events gone away. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 467–498Miller G (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL, pp 271–278Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP). 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