Suchergebnisse

[EN] We present a corpus of Spanish tweets of 15 Twitter accounts of politicians of the main five parties (PSOE, PP, Cs, UP and VOX) covering the campaign of the Spanish election of 10th November 2019 (10N Spanish Election). We perform a semi-automatic annotation of domainspecific topics using a mixture of keyword-based and supervised techniques. In this preliminary study we extracted the tweets of few politicians of each party with the aim to analyse their official communication strategy. Moreover, we analyse sentiments and emotions employed in the tweets. Although the limited size of the Twitter corpus due to the very short time span, we hope to provide with some first insights on the communication dynamics of social network accounts of these five Spanish political parties. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). ; Sánchez-Junquera, J.; Ponzetto, SP.; Rosso, P. (2020). A Twitter Political Corpus of the 2019 10N Spanish Election. Springer. 41-49. https://doi.org/10.1007/978-3-030-58323-1_4 ; S ; 41 ; 49 ; Abercrombie, G., Nanni, F., Batista-Navarro, R., Ponzetto, S.P.: Policy preference detection in parliamentary debate motions. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 249–259. Association for Computational Linguistics, November 2019 ; Ekman, P., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712 (1987) ; Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: 2015 IEEE/ACM International Conference on ASONAM, pp. 97–104. IEEE (2015) ; Glavaš, G., Nanni, F., Ponzetto, S.P.: Computational analysis of political texts: bridging research efforts across communities. In: Proceedings of the 57th Annual Meeting of the Association for ...

Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that many people receive. In this work, we propose to identify stereotypes about immigrants using two different explainable approaches: a deep learning model based on Transformers; and a text masking technique that has been recognized by its capabilities to deliver good and human-understandable results. Finally, we show the suitability of the two models for the task and offer some examples of their advantages in terms of explainability. ; Los estereotipos sobre inmigrantes son un tipo de sesgo social cada vez más presente en la interacción humana en redes sociales y en los discursos políticos. Esta desafiante tarea está siendo estudiada por la lingüística computacional debido al aumento de los mensajes de odio, el lenguaje ofensivo, y la discriminación que reciben muchas personas. En este trabajo, nos proponemos identificar estereotipos sobre inmigrantes utilizando dos enfoques diametralmente opuestos prestando atención a la explicabilidad de los mismos: un modelo de aprendizaje profundo basado en Transformers; y una técnica de enmascaramiento de texto que ha sido reconocida por su capacidad para ofrecer buenos resultados a la vez que comprensibles para los humanos. Finalmente, mostramos la idoneidad de los dos modelos para la tarea, y ofrecemos algunos ejemplos de sus ventajas en términos de explicabilidad. ; The work of the authors from the Universitat Politècnica of València was funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). Experiments were carried out on the GPU cluster at PRHLT thanks to the PROMETEO/2019/121 (DeepPattern) research project funded by the Generalitat Valenciana.

[EN] Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants¿ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of Paolo Rosso was done also in the framework of the research project PROMETEO/2019/121 (DeepPattern) funded by the Generalitat Valenciana. ; Sánchez-Junquera, J.; Chulvi-Ferriols, MA.; Rosso, P.; Ponzetto, SP. ...