Document Type : Research Paper

Authors

1 Assistant Professor of General Linguistics, Payame Noor University, Tehran, Iran

2 M.A. in General Linguistics, Payame Noor University, Tehran, Iran

3 Assistant Professor of Computer Engineering, Payame Noor University, Tehran, Iran

Abstract

The distribution of fake sport news is not based on the satisfaction of sport men, sport clubs and sport fans. Correspondingly, the identification of fake news is important and practical. This research has been done in the framework of computational linguistics. The linguistic data are based on a corpus of sports news from ISNA website and Instagram program. In this way, sports news is downloaded from the ISNA website in a period of time, and then in a few pages of the Instagram program, sports news is downloaded and compared in terms of being fake or not. The N-gram method and long and short term memory (LSTM) method have been used to identify fake news from non-fake ones. The method proposed in this paper has been implemented on four valid and existing datasets and has been compared with the previous six methods. The accuracy of this method is acceptable compared to other methods, and the results obtained indicate that this method is suitable and accurate enough to identify fake news among the news published on Instagram.

Keywords

Main Subjects

Amiri, A., Azar, A., Shahbazi, M. (2020). Stochastic and Markov chain approach to optimize re-manufacturing and outsourcing in the closed loop supply chain.. Industrial Management Studies, 18(57), 1-42.
Amirkhani, H., Jafari, M. A., Amirak, A., Pourjafari, Z., Jahromi, S. F., & Kouhkan, Z. (2020). Farstail: A Persian natural language inference dataset. arXiv preprint arXiv:2009.08820.
Chaffey, D. (2021). Global social media research summary 2016. Smart Insights: Social Media Marketing.
De Beer, D., & Matthee, M. (2020). Approaches to identify fake news: A systematic literature review. In International Conference on Integrated Science (pp. 13-22). Springer, Cham.
De Oliveira, N. R., Medeiros, D. S., & Mattos, D. M. (2020). A sensitive stylistic approach to identify fake news on social networking. IEEE Signal Processing Letters, 27, 1250-1254.
Gahirwal, M., Moghe, S., Kulkarni, T., Khakhar, D., & Bhatia, J. (2018). Fake news detection. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1), 817-819.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Book in preparation for MIT Press. URL¡ http://www. deeplearningbook. org, 1.
Jimenez, M., Maxime, C., Le Traon, Y., & Papadakis, M. (2018). On the impact of tokenizer and parameters on n-gram based code analysis. In 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 437-448).
Loyns, J. (1995). An introduction to linguistics semantics. London: Cambridge University Press.
Mertoğlu, U., & Genç, B. (2020). Automated fake news detection in the age of digital libraries. Information Technology and Libraries, 39(4). https://doi.org/10.6017/ital.v39i4.12483
Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. (2018). Fighting fake news: Image splice detection via learned self-consistency. Efros; Proceedings of the European Conference on Computer Vision (ECCV), pp. 101-117.
Nikugoftar, H., Bahrudi, A., Tokhmchi, B., Nowruzi, Gh. & Mehrgini, B. (2013). Modelling with Markov Chain; A Case Study: Stone Face of One of the Oil Sources of South West of Iran. Journal of Geotechnical Geology. (9), 1. 65-77.
Safavi, K. (2004). A survey of lexical collocation in Persian language. Literary Text Research, 7(18), 1-13. doi: 10.22054/ltr.2004.6254
Salari, M., & Adibnia, F. A. (2010). 10 Algorithms of the Bests of Data Analyses. 13th Collegiate Conference of Electricity Engineering. (24-36). Tehran: Tarbiat Modares University. 
Samani, S., & Farahani, A. (2016). Online identity and Instagram: Study of how youth present their identity on Instagram. Rasaneh, 27(2), 85-104.
Tajik Esmaeili, S., Alipour, A., Torbati, S. (2020). The role of Instagram in personal brand development (Case: Iranian Instagram Users, 2019). Communication Research, 27(103), 35-57. doi: 10.22082/cr.2020.120846.1990
Veisi, H., & Sameti, H. (2013). Speech enhancement using hidden Markov models in Mel-frequency domain. Speech Communication, 55(2), 205-220.
Zakeri, M. (2017). Determining the label of discourse segments. http: //webpages.iust.ac.ir/ morteza_zakeri/repo/iust_course_materials/NaturalLanguageProcessing/boute_grab/boute.ir_all_project_htmls/project17.html.
https://www.researchgate.net/figure/Schematic-diagram-of-a-long-short-term-memory-LSTM-activation-function_fig2_322195748