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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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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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Abstract:
[EN] Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts. Given the di culty of the task, several fact checking websites have been developed to raise awareness about which articles contain fabricated information. As a result of those platforms, several users are interested to share posts that cite evidence with the aim to refute fake news and warn other users. These users are known as fact checkers. However, there are users who tend to share false information, who can be characterised as potential fake news spreaders. In this paper, we propose the CheckerOrSpreader model that can classify a user as a potential fact checker or a potential fake news spreader. Our model is based on a Convolutional Neural Network (CNN) and combines word embeddings with features that represent users' personality traits and linguistic patterns used in their tweets. Experimental results show that leveraging linguistic patterns and personality traits can improve the performance in di erentiating between checkers and spreaders. ; The work of the first author is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). The work of Paolo Rosso is partially 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). ; Giachanou, A.; Ríssola, EA.; Ghanem, B.; Crestani, F.; Rosso, P. (2020). The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers. Springer. 181-192. https://doi.org/10.1007/978-3-030-51310-8_17 ; S ; 181 ; 192 ; Bai, S., Zhu, T., Cheng, L.: Big-Five Personality Prediction Based on User Behaviors at Social Network Sites. https://arxiv.org/abs/1204.4809 (2012) ; Bastos, M.T., Mercea, D.: The Brexit botnet and user-generated hyperpartisan news. Soc. Sci. Comput. Rev. 37(1), 38–54 (2019) ; Burbach, L., Halbach, P., Ziefle, M., Calero Valdez, A.: Who shares fake news in online social networks? In: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, pp. 234–242 (2019) ; Cer, D., et al.: Universal Sentence Encoder. https://arxiv.org/abs/1803.11175 (2018) ; DiFranzo, D., Gloria, M.J.K.: Filter Bubbles and Fake News. ACM Crossroads 23(3), 32–35 (2017) ; Farías, D.I.H., Patti, V., Rosso, P.: Irony detection in Twitter: the role of affective content. ACM Trans. Internet Technol. (TOIT) 16(3), 1–24 (2016) ; Ghanem, B., Glavaš, G., Giachanou, A., Paolo, S., Ponzetto, P.R., Rangel, F.: UPV-UMA at CheckThat! lab: verifying Arabic claims using a cross lingual approach. In: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum (2019) ; Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020) ; Giachanou, A., Gonzalo, J., Crestani, F.: Propagating sentiment signals for estimating reputation polarity. Inf. Process. Manage. 56(6), 102079 (2019) ; Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 877–880 (2019) ; Goldberg, L.R.: A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. Pers. Psychol. Europe 7(1), 7–28 (1999) ; Heinström, J.: Five personality dimensions and their influence on information behaviour. Inf. Res. 9(1), 1–9 (2003) ; John, O.P., Srivastava, S.: The big-five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, pp. 102–138 (1999) ; Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34 (2010) ; Neuman, Y.: Computational Personality Analysis: Introduction, Practical Applications and Novel Directions, 1st edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-42460-6 ; Neuman, Y., Cohen, Y.: A vectorial semantics approach to personality assessment. Sci. Rep. 4(1), 1–6 (2014) ; Oyeyemi, S.O., Gabarron, E., Wynn, R.: Ebola, Twitter, and misinformation: a dangerous combination? BMJ Clin. Res. 349, g6178 (2014) ; Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The Development and Psychometric Properties of LIWC 2015. Technical report (2015) ; Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 1532–1543 (2014) ; Pennycook, G., Rand, D.: Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J. Pers. 88, 185–200 (2018) ; Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1589–1599 (2011) ; Rangel, F., Rosso, P.: Overview of the 7th author profiling task at PAN 2019: bots and gender profiling in Twitter. In: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum (2019) ; Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937 (2017) ; Ríssola, E.A., Bahrainian, S.A., Crestani, F.: Personality recognition in conversations using capsule neural networks. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, pp. 180–187 (2019) ; Ross, C., Orr, E.S., Sisic, M., Arseneault, J.M., Simmering, M.G., Orr, R.R.: Personality and motivations associated with Facebook use. Comput. Hum. Behav. 25(2), 578–586 (2009) ; Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018, pp. 430–435 (2018) ; Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. https://arxiv.org/abs/1809.01286 (2018) ; Vo, N., Lee, K.: Learning from fact-checkers: analysis and generation of fact-checking language. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 335–344 (2019) ; Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018) ; Wang, W.Y.: Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection. https://arxiv.org/abs/1705.00648 (2017)
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Keyword:
Fact checkers detection; LENGUAJES Y SISTEMAS INFORMATICOS; Linguistic patterns; Personality traits
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URL: https://doi.org/10.1007/978-3-030-51310-8_17
http://hdl.handle.net/10251/179851
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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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On the Use of Character n-grams as the only Intrinsic Evidence of Plagiarism
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Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter
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On the use of word embedding for cross language plagiarism detection
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Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection
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Improving Attitude Words Classification for Opinion Mining using Word Embedding
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Classifier combination approach for question classification for Bengali question answering system
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Paraphrase Plagiarism Identifcation with Character-level Features
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A Low Dimensionality Representation for Language Variety Identification
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