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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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A Twitter Political Corpus of the 2019 10N Spanish Election
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Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
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Multimodal Fake News Detection with Textual, Visual and Semantic Information
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An Emotional Analysis of False Information in Social Media and News Articles
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| 26 |
Irony Detection in Twitter with Imbalanced Class Distributions
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| 27 |
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Fine-Grained Analysis of Language Varieties and Demographics
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Multilingual Stance Detection in Social Media Political Debates
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Abstract:
[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. https://doi.org/10.1016/j.csl.2020.101075 ; S ; 1 ; 27 ; 63 ; Balahur, A., & Turchi, M. (2014). Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28(1), 56-75. doi:10.1016/j.csl.2013.03.004 ; 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 ; Boiy, E., & Moens, M.-F. (2008). A machine learning approach to sentiment analysis in multilingual Web texts. Information Retrieval, 12(5), 526-558. doi:10.1007/s10791-008-9070-z ; DellaPosta, D., Shi, Y., & Macy, M. (2015). Why Do Liberals Drink Lattes? American Journal of Sociology, 120(5), 1473-1511. doi:10.1086/681254 ; Küçük, D., Can, F., 2019. A tweet dataset annotated for named entity recognition and stance detection. arXiv preprint arXiv:1901.04787. Available at: https://arxiv.org. ; 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 ; 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 ; Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(3). doi:10.1103/physreve.76.036106 ; Vychegzhanin, S. V., & Kotelnikov, E. V. (2019). Stance Detection Based on Ensembles of Classifiers. Programming and Computer Software, 45(5), 228-240. doi:10.1134/s0361768819050074 ; West, D. M. (1991). Polling effects in election campaigns. Political Behavior, 13(2), 151-163. doi:10.1007/bf00992294 ; 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 ; Zappavigna, M. (2015). Searchable talk: the linguistic functions of hashtags. Social Semiotics, 25(3), 274-291. doi:10.1080/10350330.2014.996948
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Keyword:
Contextual features; LENGUAJES Y SISTEMAS INFORMATICOS; Multilingual; Political debates; Stance detection; Twitter
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URL: https://doi.org/10.1016/j.csl.2020.101075
http://hdl.handle.net/10251/166374
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| 31 |
Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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