The Use of Syntactic Information in Fake News Detection: A Systematic Review
DOI:
https://doi.org/10.5753/reviews.2024.2718Keywords:
Fake news, Syntax, Syntactic informationAbstract
Fake news has been a critical problem for society, to the extent that its damaging effects can already be seen in several areas, such as democracy and health. However, as fake news grow in number, manual fact-checking becomes impractical for identifying them, which makes automatic detection a compelling alternative. In this sense, this study gathers multiple solutions for the problem of automatically detecting fake news, through the usage of both lexical and syntactic information. This study consists of a systematic review on fake news detection through linguistic patterns, focusing on the use of syntax to aid in the task. Solving complex problems by capturing linguistic patterns is mostly explored in the Natural Language Processing (NLP) area. In general, the use of shallow syntax representations, such as Parts of speech, only marginally increases the performance of classifiers in this task. However, relying on deeper syntactic representations, such as context-free grammars or syntactic dependency trees, present more promising results.
Downloads
References
Agarwal, A. and Dixit, A. (2020). Fake news detection: An ensemble learning approach. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1178-1183, Madurai, India. IEEE. DOI: 10.1109/ICICCS48265.2020.9121030.
Allcott, H. and Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2):211-36. DOI: 10.1257/jep.31.2.211.
Balwant, M. K. (2019). Bidirectional lstm based on pos tags and cnn architecture for fake news detection. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1-6, Kanpur, India. IEEE. DOI: 10.1109/ICCCNT45670.2019.8944460.
Choudhary, A. and Arora, A. (2021). Linguistic feature based learning model for fake news detection and classification. Expert Systems with Applications, 169:114-171. DOI: 10.1016/j.eswa.2020.114171.
Cignarella, A. T., Basile, V., Sanguinetti, M., Bosco, C., Rosso, P., and Benamara, F. (2020). Multilingual irony detection with dependency syntax and neural models. In Scott, D., Bel, N., and Zong, C., editors, Proceedings of the 28th International Conference on Computational Linguistics, pages 1346-1358, Barcelona, Spain (Online). International Committee on Computational Linguistics. DOI: 10.18653/v1/2020.coling-main.116.
Coste, C. I., Bufnea, D., and Niculescu, V. (2020). A new language independent strategy for clickbait detection. In 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1-6, Split, Croatia. IEEE. DOI: 10.23919/SoftCOM50211.2020.9238342.
De Sarkar, S., Yang, F., and Mukherjee, A. (2018). Attending sentences to detect satirical fake news. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3371-3380, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Available online [link].
Gupta, S., Singh, P., Sundriyal, M., Akhtar, M. S., and Chakraborty, T. (2021). Lesa: Linguistic encapsulation and semantic amalgamation based generalised claim detection from online content. DOI: 10.48550/arXiv.2101.11891.
Han, W. and Mehta, V. (2019). Fake news detection in social networks using machine learning and deep learning: Performance evaluation. In 2019 IEEE International Conference on Industrial Internet (ICII), pages 375-380, Orlando, USA. IEEE. DOI: 10.1109/ICII.2019.00070.
Hossain, M. Z., Rahman, M. A., Islam, M. S., and Kar, S. (2020). BanFakeNews: A dataset for detecting fake news in Bangla. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 2862-2871, Marseille, France. European Language Resources Association. Available online [link].
Joo, Y. and Hwang, I. (2019). Steve Martin at SemEval-2019 task 4: Ensemble learning model for detecting hyperpartisan news. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 990-994, Minneapolis, Minnesota, USA. Association for Computational Linguistics. DOI: 10.18653/v1/S19-2171.
Kapusta, J., Drlik, M., and Munk, M. (2021). Using of n-grams from morphological tags for fake news classification. PeerJ Computer Science, 7:1-27. DOI: 10.7717/PEERJ-CS.624.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1-26. Available online [link].
Klein, D. and Manning, C. D. (2003). Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pages 423-430, Sapporo, Japan. Association for Computational Linguistics. DOI: 10.3115/1075096.1075150.
Kuzmin, G., Larionov, D., Pisarevskaya, D., and Smirnov, I. (2020). Fake news detection for the Russian language. In Aker, A. and Zubiaga, A., editors, Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 45-57, Barcelona, Spain (Online). Association for Computational Linguistics. Available online [link].
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., and Zittrain, J. L. (2018). The science of fake news. Science, 359(6380):1094-1096. DOI: 10.1126/science.aao2998.
Moscadelli, A., Albora, G., Biamonte, M. A., Giorgetti, D., Innocenzio, M., Paoli, S., Lorini, C., Bonanni, P., and Bonaccorsi, G. (2020). Fake news and covid-19 in italy: Results of a quantitative observational study. International Journal of Environmental Research and Public Health, 17. DOI: 10.3390/ijerph17165850.
Moschitti, A. (2006). Efficient convolution kernels for dependency and constituent syntactic trees. In Fürnkranz, J., Scheffer, T., and Spiliopoulou, M., editors, Machine Learning: ECML 2006, pages 318-329, Berlin, Heidelberg. Springer Berlin Heidelberg. DOI: 10.1007/11871842_32.
Nguyen, D.-V., Dang, T., and Nguyen, N. (2019). NLP@UIT at SemEval-2019 task 4: The paparazzo hyperpartisan news detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 971-975, Minneapolis, Minnesota, USA. Association for Computational Linguistics. DOI: 10.18653/v1/S19-2167.
Oshikawa, R., Qian, J., and Wang, W. Y. (2020). A survey on natural language processing for fake news detection. DOI: 10.48550/arXiv.1811.00770.
Paixão, M., Lima, R., and Espinasse, B. (2020). Fake news classification and topic modeling in brazilian portuguese. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 427-432, Melbourne, Australia. IEEE. DOI: 10.1109/WIIAT50758.2020.00063.
Pérez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, R. (2018). Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391-3401, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Available online [link].
Pisarevskaya, D. (2017). Deception detection in news reports in the Russian language: Lexics and discourse. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 74-79, Copenhagen, Denmark. Association for Computational Linguistics. DOI: 10.18653/v1/W17-4213.
Qiao, Y., Wiechmann, D., and Kerz, E. (2020). A language-based approach to fake news detection through interpretable features and BRNN. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 14-31, Barcelona, Spain (Online). Association for Computational Linguistics. Avalable at: [link].
Reis, J. C. S., Correia, A., Murai, F., Veloso, A., and Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2):76-81. DOI: 10.1109/MIS.2019.2899143.
Rubin, V., Conroy, N., Chen, Y., and Cornwell, S. (2016). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pages 7-17, San Diego, California. Association for Computational Linguistics. DOI: 10.18653/v1/W16-0802.
Rubin, V. L., Chen, Y., and Conroy, N. J. (2015). Deception detection for news: Three types of fakes. In Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, ASIST '15, USA. American Society for Information Science. DOI: 10.1002/pra2.2015.145052010083.
Sabeeh, V., Zohdy, M., and Bashaireh, R. A. (2019). Enhancing the fake news detection by applying effective feature selection based on semantic sources. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1365-1370, Las Vegas, USA. IEEE. DOI: 10.1109/CSCI49370.2019.00255.
Santos, R., Pedro, G., Leal, S., Vale, O., Pardo, T., Bontcheva, K., and Scarton, C. (2020). Measuring the impact of readability features in fake news detection. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1404-1413, Marseille, France. European Language Resources Association. Available online [link].
Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017a). Fake news detection on social media: A data mining perspective. SIGKDD Explor. Newsl., 19(1):22–36. DOI: 10.1145/3137597.3137600.
Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017b). Fake news detection on social media: A data mining perspective. SIGKDD Explor. Newsl., 19(1):22–36. DOI: 10.1145/3137597.3137600.
Silva, R. M., Santos, R. L., Almeida, T. A., and Pardo, T. A. (2020). Towards automatically filtering fake news in portuguese. Expert Systems with Applications, 146:113199. DOI: 10.1016/j.eswa.2020.113199.
Vincze, V. and Szabó, M. K. (2020). Automatic detection of Hungarian clickbait and entertaining fake news. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 58-69, Barcelona, Spain (Online). Association for Computational Linguistics. Available online [link].
Volkova, S., Shaffer, K., Jang, J. Y., and Hodas, N. (2017). Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 647-653, Vancouver, Canada. Association for Computational Linguistics. DOI: 10.18653/v1/P17-2102.
Vosoughi, S., Roy, D., and Aral, S. (2018). The spread of true and false news online. Science, 359(6380):1146-1151. DOI: 10.1126/science.aap9559.
Zannettou, S., Sirivianos, M., Blackburn, J., and Kourtellis, N. (2019). The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. J. Data and Information Quality, 11(3). DOI: 10.1145/3309699.
Zewdu, A. and Yitagesu, B. (2022). Part of speech tagging: a systematic review of deep learning and machine learning approaches. Journal of Big Data, 9. DOI: 10.1186/s40537-022-00561-y.
Zhou, X., Jain, A., Phoha, V. V., and Zafarani, R. (2020). Fake news early detection: A theory-driven model. Digital Threats: Research and Practice, 1(2). DOI: 10.1145/3377478.
Zhou, X. and Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Comput. Surv., 53(5). DOI: 10.1145/3395046.
Zhou, X., Zafarani, R., Shu, K., and Liu, H. (2019). Fake news: Fundamental theories, detection strategies and challenges. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM '19, page 836–837, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3289600.3291382.
