Leveraging the Natural Language Processing (NLP) Tools and Techniques in the Effective Detection of Fake News

Samriti Dhamija

Vol. 7, Issue 1, Jan-Dec 2021

Page Number: 126 - 134

Abstract:

Fake news is inauthentic or misleading data. However, it is accounted as news. The human way of behaving impacts the tendency for individuals to spread misleading data; research shows that people are attracted to unanticipated new occasions and data, which increases the activity of the brain. Moreover, found that forced thinking helps spread inaccurate data. This urges people to repost or scatter misleading substance, often distinguished by misleading content and eye-catching names. The proposed engagement uses AI and natural language processing (NLP) ways to deal with recognizing fake news, explicitly, things from dubious sources. The dataset is ISOT, which contains Fake news gathered from different sources. Web mining is used here to remove the text from news sites to gather the ongoing news and is added to the dataset. Pre-processing of data and component extraction is applied to the information. It is followed by dimensionality reduction and order utilizing models using classifiers like Rocchio classification, Bagging, Passive Aggressive and Gradient Boosting. We determined a few analyses to pick the best working model with accurate anticipation for counterfeit news.

References

• Ahmad, Iftikhar, Muhammad Yousaf, Suhail Yousaf, and Muhammad Ovais Ahmad. 'Fake news detection using machine learning ensemble methods.' Complexity 2020 (2020).
• Dinesh, T., and T. Rajendran. 'Higher classification of fake political news using decision tree algorithm over naive Bayes algorithm.' REVISTA GEINTECGESTAO INOVACAO E TECNOLOGIAS 11, no. 2 (2021): 1084-1096.
• Yazdi, Kasra Majbouri, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, and Saeed Saedy. 'Improving fake news detection using k-means and support vector machine approaches.' International Journal of Electronics and Communication Engineering 14, no. 2 (2020): 38-42.
• Aldwairi, Monther, and Ali Alwahedi. 'Detecting fake news in social media networks.' Procedia Computer Science 141 (2018): 215-222
• Zhang, Jiawei, Bowen Dong, and S. Yu Philip. 'Fakedetector: Effective fake news detection with deep diffusive neural network.' In 2020 IEEE 36th international conference on data engineering (ICDE), pp. 1826-1829. IEEE, 2020.
• Parikh, Shivam B., and Pradeep K. Atrey. 'Mediarich fake news detection: A survey.' In 2018 IEEE conference on multimedia information processing and retrieval (MIPR), pp. 436-441. IEEE, 2018.
• Shu, Kai, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 'Fake news detection on social media: A data mining perspective.' ACM SIGKDD explorations newsletter 19, no. 1 (2017): 22-36.
• Zhang, Xichen, and Ali A. Ghorbani. 'An overview of online fake news: Characterization, detection, and discussion.' Information Processing & Management 57, no. 2 (2020): 102025.
• Rubin, Victoria L., Niall J. Conroy, and Yimin Chen. 'Towards news verification: Deception detection methods for news discourse.' In Hawaii International Conference on System Sciences, pp. 5-8. 2015.
• Chen, Yimin, Niall J. Conroy, and Victoria L. Rubin. 'Misleading online content: recognizing clickbait as' false news'.' In Proceedings of the 2015 ACM on workshop on multimodal deception detection, pp. 15-19. 2015.
• Zhang, Xichen, and Ali A. Ghorbani. 'An overview of online fake news: Characterization, detection, and discussion.' Information Processing & Management 57, no. 2 (2020): 102025.
• Zhou, Xinyi, and Reza Zafarani. 'A survey of fake news: Fundamental theories, detection methods, and opportunities.' ACM Computing Surveys (CSUR) 53, no. 5 (2020): 1-40.
• Reis, Julio CS, André Correia, Fabrício Murai, Adriano Veloso, and Fabrício Benevenuto. 'Supervised learning for fake news detection.' IEEE Intelligent Systems 34, no. 2 (2019): 76-81.
• Singhal, Shivangi, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, and Shin'ichi Satoh. 'Spotfake: A multi-modal framework for fake news detection.' In 2019 IEEE fifth international conference on multimedia big data (BigMM), pp. 39-47. IEEE, 2019.
• Oshikawa, Ray, Jing Qian, and William Yang Wang. 'A survey on natural language processing for fake news detection.' arXiv preprint arXiv:1811.00770 (2018).

Back Download