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<dc:title>#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection</dc:title>
<dc:creator>Lai, Mirko</dc:creator>
<dc:creator>Patti, Viviana</dc:creator>
<dc:creator>Ruffo, Giancarlo</dc:creator>
<dc:creator>Rosso, Paolo</dc:creator>
<dc:contributor>Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació</dc:contributor>
<dc:contributor>GENERALITAT VALENCIANA</dc:contributor>
<dc:contributor>AGENCIA ESTATAL DE INVESTIGACION</dc:contributor>
<dc:contributor>Università degli Studi di Torino</dc:contributor>
<dc:subject>Stance detection</dc:subject>
<dc:subject>Twitter</dc:subject>
<dc:subject>Brexit</dc:subject>
<dc:subject>NLP</dc:subject>
<dc:subject>Community detection</dc:subject>
<dc:subject>LENGUAJES Y SISTEMAS INFORMATICOS</dc:subject>
<dc:description>[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.</dc:description>
<dc:description>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).</dc:description>
<dc:description>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</dc:description>
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<dc:creator>Patti, Viviana</dc:creator>
<dc:creator>Ruffo, Giancarlo</dc:creator>
<dc:creator>Rosso, Paolo</dc:creator>
<dc:contributor>Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació</dc:contributor>
<dc:contributor>GENERALITAT VALENCIANA</dc:contributor>
<dc:contributor>AGENCIA ESTATAL DE INVESTIGACION</dc:contributor>
<dc:contributor>Università degli Studi di Torino</dc:contributor>
<dc:subject>Stance detection</dc:subject>
<dc:subject>Twitter</dc:subject>
<dc:subject>Brexit</dc:subject>
<dc:subject>NLP</dc:subject>
<dc:subject>Community detection</dc:subject>
<dc:subject>LENGUAJES Y SISTEMAS INFORMATICOS</dc:subject>
<dc:description>[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.</dc:description>
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<dc:title>#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection</dc:title>
<dc:creator>Lai, Mirko</dc:creator>
<dc:creator>Patti, Viviana</dc:creator>
<dc:creator>Ruffo, Giancarlo</dc:creator>
<dc:creator>Rosso, Paolo</dc:creator>
<dc:contributor>Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació</dc:contributor>
<dc:contributor>GENERALITAT VALENCIANA</dc:contributor>
<dc:contributor>AGENCIA ESTATAL DE INVESTIGACION</dc:contributor>
<dc:contributor>Università degli Studi di Torino</dc:contributor>
<dc:subject>Stance detection</dc:subject>
<dc:subject>Twitter</dc:subject>
<dc:subject>Brexit</dc:subject>
<dc:subject>NLP</dc:subject>
<dc:subject>Community detection</dc:subject>
<dc:subject>LENGUAJES Y SISTEMAS INFORMATICOS</dc:subject>
<dc:description>[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.</dc:description>
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