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Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate ...
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PyPlutchik: Visualising and comparing emotion-annotated corpora
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In: PLoS One (2021)
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“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media
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#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Abstract:
[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. , Rethinking context: Language as an interactive phenomenon, Cambridge University Press, (1992). ; Evans A. , Stance and identity in Twitter hashtags, Language@ Internet 13(1) (2016). ; Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174. doi:10.1016/j.physrep.2009.11.002 ; Gelman, A., & King, G. (1993). Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable? British Journal of Political Science, 23(4), 409-451. doi:10.1017/s0007123400006682 ; Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number. PLoS ONE, 6(8), e22656. doi:10.1371/journal.pone.0022656 ; González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779-782. doi:10.1038/nature06958 ; Hernández-Castañeda, Á., Calvo, H., & Gambino, O. J. (2018). Impact of polarity in deception detection. Journal of Intelligent & Fuzzy Systems, 35(1), 549-558. doi:10.3233/jifs-169610 ; Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721-723. doi:10.1126/science.1167742 ; Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and Sentiment in Tweets. ACM Transactions on Internet Technology, 17(3), 1-23. doi:10.1145/3003433 ; Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.x ; Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011 ; Pennebaker J.W. , Francis M.E. and Booth R.J. , Linguistic Inquiry and Word Count: LIWC 2001, Mahway: Lawrence Erlbaum Associates 71 (2001). ; Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035 ; Theocharis, Y., & Lowe, W. (2015). Does Facebook increase political participation? Evidence from a field experiment. Information, Communication & Society, 19(10), 1465-1486. doi:10.1080/1369118x.2015.1119871 ; Whissell, C. (2009). Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105(2), 509-521. doi:10.2466/pr0.105.2.509-521
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Keyword:
Brexit; Community detection; LENGUAJES Y SISTEMAS INFORMATICOS; NLP; Stance detection; Twitter
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URL: https://doi.org/10.3233/JIFS-179895
http://hdl.handle.net/10251/170080
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not
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Figurative Messages and Affect in Twitter: Differences Between #irony, #sarcasm and #not
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ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm
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