Automated Fake News Detection System

Authors

  • Pushpanjali S. Sajjanshetti NBN Sinhgad School of Engineering/Savitribai Phule Pune University, India
  • Ashish Kumar Gupta NBN Sinhgad School of Engineering/Savitribai Phule Pune University, India
  • Prince Kumar NBN Sinhgad School of Engineering/Savitribai Phule Pune University, India
  • Ayush Gautam NBN Sinhgad School of Engineering/Savitribai Phule Pune University, India
  • Ritik Kumar NBN Sinhgad School of Engineering/Savitribai Phule Pune University, India

Keywords:

Fake news, social media, dataset, machine learning, deep learning

Abstract

The classification of fake news on social media platforms has drawn a lot of attention recently because it is so simple to put false stuff there. In addition, they prefer social media to traditional television as their news source of choice. These patterns have increased academics' understanding of fake news and inspired an increase in interest in its identification. The primary goal of this work was to classify incorrect information found on text-based social media platforms (text classification). The false news dataset was categorized in this classification using ten different machine learning and deep learning classifiers. Four traditional techniques (term frequency-inverse document frequency, count vector, character level vector, and N-Gram level vector) were used to extract features from texts.

References

Z. Zhe, P. Resnick, and Q. Mei. "Enquiring minds: Early detection of rumors in social media from enquiry posts." Proceedings of the 24th international conference on world wide web. 2015.

M. Jing, et al. "Detecting rumors from microblogs with recurrent neural networks." (2016): 3818.

C. Carlos, M. Mendoza, and Barbara Poblete. "Information credibility on twitter." Proceedings of the 20th international conference on World wide web. 2011.

Granik M, Mesyura V. Fake news detection using naive Bayes classifier. In2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) 2017 May 29 (pp. 900-903). IEEE.

M.Granik, M ykhailo, and V. Mesyura. "Fake news detection using naive Bayes classifier." 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, 2017.

P. Bharadwaj, and Z. Shao. "Fake news detection with semantic features and text mining." International Journal on Natural Language Computing (IJNLC) Vol 8 (2019).

S. Helmstetter, and H. Paulheim. "Weakly supervised learning for fake news detection on Twitter." 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018.

T. Chen, et al. "Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection." Pacific-Asia conference on knowledge discovery and data mining. Springer, Cham, 2018.

Ma J, Gao W, Wong KF. Detect rumors in microblog posts using propagation structure via kernel learning. Association for Computational Linguistics. 2017.

Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection. In Proceedings of the twelfth ACM international conference on web search and data mining 2019 Jan 30 (pp. 312–320).

M. Rahul R., et al. "Identification of Fake News Using Machine Learning." 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2020.

Kaur S, Kumar P, Kumaraguru P. Automating fake news detection system using multi-level voting model. Soft Computing. 2020 Jun;24(12):9049-69.

Roy, Arjun, et al. "A deep ensemble framework for fake news detection and classification." arXiv preprint arXiv:1811.04670 (2018). 14. Kaliyar RK. Fake news detection using a deep neural network. In2018 4th International Conference on Computing Communication and Automation (ICCCA) 2018 Dec 14 (pp. 1-7). IEEE.

Y. Liu, and Yi-Fang Brook Wu. "Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks." Thirty-second AAAI conference on artificial intelligence. 2018.

Karimi H, Roy P, Saba-Sadiya S, Tang J. Multi-source multi-class fake news detection. In Proceedings of the 27th international conference on computational linguistics 2018 Aug (pp. 1546-1557).

A. Farzana Islam, et al. "Effect of Corpora on Classification of Fake News using Naive Bayes Classifier." International Journal of Automation, Artificial Intelligence and Machine Learning 1.1 (2020): 80-92.

Team D. Detecting Fake News with Python and Machine Learning - DataFlair. 2019. Available from: https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/

S. Shikhar, R. Kiros, and Ruslan Salakhutdinov. "Action recognition using visual attention." arXiv preprint arXiv:1511.04119 (2015).

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Published

2023-06-27

Issue

Vol. 14 No. 1 (2023)

Section

Articles

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