Abstract
Fake news, or false information presented as news, is an increasing risk in today’s society. The practice of automatically detecting fake news is by no means an easy task, since the authors of fake news intend to confuse the readers and make them vulnerable to false information. Traditional methods only consider a limited number of characteristics of fake news, and hence, they face many difficulties in predicting the credibility of the news. This paper proposes WES, an integrated stacking model where the multiple-view text representation from (i) Word-level features, (ii) Emotional features, and (iii) Sentence-level features are used to classify the news article. The proposed system is applied on a real-world dataset, FakeNewsNet, and the experimental results show that the proposed approach achieves significantly better performance than the current state-of-the-art fake news detection method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
Chen, Y., Conroy, N.J., Rubin, V.L.: Misleading online content: recognizing clickbait as “false news”. In: Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection, pp. 15–19 (2015)
Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS ONE 10(6), e0128193 (2015)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186 (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, pp. 877–880 (2019)
Hassan, N., Li, C., Tremayne, M.: Detecting check-worthy factual claims in presidential debates. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1835–1838 (2015)
Jin, Z., Cao, J., Jiang, Y., Zhang, Y.: News credibility evaluation on microblog with a hierarchical propagation model. In: 2014 IEEE International Conference on Data Mining, pp. 230–239 (2014)
Jin, Z., Cao, J., Zhang, Y., Luo, J.: News verification by exploiting conflicting social viewpoints in microblogs. In: AAAI 2016 (2016)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. (TOIT) 17(3), 1–23 (2017)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., Stein, B.: A stylometric inquiry into hyperpartisan and fake news. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 231–240 (2018)
Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806 (2017)
Shi, B., Weninger, T.: Fact checking in heterogeneous information networks. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 101–102 (2016)
Shu, K., Cui, L., Wang, S., Lee, D., Liu, H.: Defend: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 395–405 (2019)
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: Fakenewsnet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171–188 (2020)
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)
Tacchini, E., Ballarin, G., Vedova, M.L.D., Moret, S., de Alfaro, L.: Some like it hoax: automated fake news detection in social networks. CoRR abs/1704.07506 (2017)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ha, T., Gao, X. (2021). Fake News Detection Using Multiple-View Text Representation. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-89363-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89362-0
Online ISBN: 978-3-030-89363-7
eBook Packages: Computer ScienceComputer Science (R0)