Suchergebnisse

PhD thesis in Computer Science has been written by Mirko Lai under the supervision of Prof. Paolo Rosso (Universitat Politècnica de València), Dr. Giancarlo Ruffo (University of Turin) and Dr. Viviana Patti (University of Turin). This thesis was developed under a cotutelle between the Universitat Politècnica de València, Spain and the University of Turin, Italy. The thesis defense was done in Turin, Italy on February 11, 2019. The doctoral committee was integrated by: Leo Ferres (Universidad del Desarrollo, Chile), Delfina Malandrina (Università degli Studi di Salerno, Italy) and Sara Tonelli (Fondazione Bruno Kessler, Italy). ; Tesis doctoral en Informática ha sido realizada por Mirko Lai y dirigida por el Prof. Paolo Rosso (Universitat Politècnica de València), Dr. Giancarlo Ruffo (University of Turin) y la Dra. Viviana Patti (University of Turin) en el marco de un convenio de cotutela entre la Universitat Politècnica de València, España y la Universidad de Turin, Italia. La defensa de la tesis fue en Turin, Italia el 11 de febrero de 2019 ante un tribunal compuesto por: Leo Ferres (Universidad del Desarrollo, Chile), Delfina Malandrina (Università degli Studi di Salerno, Italia) y Sara Tonelli (Fondazione Bruno Kessler, Italia).

Tesis por compendio ; [ES] En esta tesis abordamos el problema de la detección de las opiniones (stance detection, SD) en las redes sociales, centrándose en los debates políticos polarizados en Twitter. La SD consiste en determinar automáticamente si el autor de una publicación está a favor o en contra de un objetivo de interés, o si no se puede inferir la opinión. Nos ocupamos de temas políticos como las elecciones políticas y los referendos y, como resultado, los objetivos son tanto personas como referendos. También exploramos las comunicaciones que tienen lugar en estos debates polarizados, arrojando luz sobre las dinámicas de comunicación entre personas que tienen opiniones en acuerdo o en conflicto, enfocándonos en particular en la observación del cambio de opiniones (opinion shifting). Proponemos modelos de aprendizaje automático para la SD como si fuera un problema de clasificación binaria. Exploramos características basadas en el contenido del texto del tweet, además usamos características basadas en información contextual que no emerge directamente del texto. Utilizando el corpus de benchmark propuesto para la tarea compartida sobre la SD realizado para SemEval 2016, exploramos la contribución que el estudio de las relaciones entre el objetivo de interés y las otras entidades involucradas en el debate proporciona a la SD. Al participar en la tarea ``Stance and Gender Detection in Tweets on Catalan Independence'' organizado para IberEval 2017, hemos propuesto otras características textuales y contextuales para la SD en tweets en español y en catalán. Explorando la SD desde una perspectiva multilingüe, hemos creado un corpus de tweets en francés y uno en italiano. La extensión multilingüe de nuestro modelo (multiTACOS) muestra que la SD está influenciada más por los diferentes estilos utilizados por los usuarios para comunicar la opinión sobre objetivos de diferentes tipos (personas o referendos) en lugar del idioma utilizado. Con el objetivo de recuperar información contextual sobre la red social de los ...

[EN] Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users¿ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users¿ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance. ; The work of P. Rosso was partially funded by the Spanish MICINN under the research projects MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech(PGC2018-096212-B-C31) and PROMETEO/2019/121 (DeepPattern) of the Generalitat Valenciana. The work of V. Patti and G. Ruffo was partially funded by Progetto di Ateneo/CSP 2016 Immigrants, Hate and Prejudice in Social Media (S1618 L2 BOSC 01). ; Lai, M.; Patti, V.; Ruffo, G.; Rosso, P. (2020). #Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection. Journal of Intelligent & Fuzzy Systems. 39(2):2341-2352. https://doi.org/10.3233/JIFS-179895 ; S ; 2341 ; 2352 ; 39 ; 2 ; Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008 ; Deitrick, W., & Hu, W. (2013). Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 01(03), 19-29. doi:10.4236/jdaip.2013.13004 ; Duranti A. and Goodwin C. , ...

[EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day. ; The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the "Progetto di ...

1 20 124 ; S ; [EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day. The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the ...

The paper introduces a new annotated Spanish and Catalan data set for Sentiment Analysis about the Catalan separatism and the related debate held in social media at the end of 2015. It focuses on the collection of data, where we dealt with the exploitation in the debate of two languages, i.e. Spanish and Catalan, and on the design of the annotation scheme, previously applied in the development of other corpora about political debates, which extends a polarity label set by making available tags for irony and semantic oriented labels. The annotation process is presented and the detected disagreement discussed.

[EN] Stance Detection is the task of automatically determining whether the author of a text is in favor, against, or neutral towards a given target. In this paper we investigate the portability of tools performing this task across different languages, by analyzing the results achieved by a Stance Detection system (i.e. MultiTACOS) trained and tested in a multilingual setting. First of all, a set of resources on topics related to politics for English, French, Italian, Spanish and Catalan is provided which includes: novel corpora collected for the purpose of this study, and benchmark corpora exploited in Stance Detection tasks and evaluation exercises known in literature. We focus in particular on the novel corpora by describing their development and by comparing them with the benchmarks. Second, MultiTACOS is applied with different sets of features especially designed for Stance Detection, with a specific focus to exploring and combining both features based on the textual content of the tweet (e.g., style and affective load) and features based on contextual information that do not emerge directly from the text. Finally, for better highlighting the contribution of the features that most positively affect system performance in the multilingual setting, a features analysis is provided, together with a qualitative analysis of the misclassified tweets for each of the observed languages, devoted to reflect on the open challenges. ; Cristina Bosco and Viviana Patti are partially supported by Progetto di Ateneo/CSP 2016 (Immigrants, Hate and Prejudice in Social Media, S1618_L2_BOSC_01). The work of Paolo Rosso was partially funded bythe Spanish MICINN under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018096212-B-C31). ; Lai, M.; Cignarella, AT.; Hernandez-Farias, DI.; Bosco, C.; Patti, V.; Rosso, P. (2020). Multilingual Stance Detection in Social Media Political Debates. Computer Speech & Language. 63:1-27. ...