Text Analytics Solutions for the Control of Fake News: Materials and Methods

Emeka Ogbuju, Taiwo Abiodun, Francisca Oladipo

Abstract


The increase in the rate of internet and social media use has given rise to a lot of fake news and misinformation available online. The internet and social media have made information and communications flow to be faster and easier. On the other hand, the internet and social media have also jeopardized the authenticity of the news that is being sent online, as it has given people the opportunity to intentionally spread fake news. This has caused a lot of social and national damage with destructive impacts. Hence, there is a need to apply data mining and text analytic techniques in the detection of fake news across news agencies that operate online. Literature has shown that the use of data mining and text analytic techniques can play important role in both the detection of fake news and the blockage of it. The leading data mining and text analytic techniques used in fake news detection are described in this paper by answering three (3) research questions from papers between 2017 to 2022 alongside recommendations for applications for newsagents. The result presents fourteen (14) techniques and twenty (20) state of the arts datasets for fake news research.

Full Text:

PDF

References


Maciej, S. (2018). FakeNewsCorpus: A dataset of millions of news articles scraped from a curated list of data sources. Retrieved from github:

Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019). Fake news: Fundamental theories, detection strategies and challenges. Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 836-837). Association for Computing Machinery, Inc.

Donepudi, P. K. (2019). Automation and Machine Learning in Transforming the Financial Industry. Asian Business Review, 129-138.

Alzubi, Jafar., Nayyar, Anand., & Kumar, Akshi (2018). Machine learning from theory to algorithms: an overview. Journal of Physics Conference Series.

Aniyath, A. (2019). A Survey on Fake Newa Detection by the Data Mining Perspective. International Journal of Information and Computer Science, 9-28.

Della, V. M., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., & De Alfaro, L. (2018). Automatic online fake news detection combining content and social signals. Proceedings of the 22st Conference of Open Innovations Association FRUCT (pp. 272–279). IEEE.

Kurasinski, L., & Mihailescu, C. (2020). Machine Learning explainability in text classification for Fake News detection. 19th IEEE International Conference on Machine Learning (pp. 775-781). IEEE.

Maniz, S. (2018). Detecting FAke News with Sentiment Analysis and Network Metadata. Earlham Historical Journal.

Reddy, H., Raj, N., Manali, G., & Basava, A. (2020). Tex-mining-based Fake News Deetection Using Ensemble Methods. International Journal of Automation and Computing, 210-221.

Khanam, Z., Alwasel, B. N., Sirafi, H., & Rashid, M. (2021). Fake News Detection Using Machine Learning Approaches. IOP Conference Series: Materials Science and Engineering. IOP.

Iftikhar, A., Muhammad Yousaf, Sukail, Y., & Muhammad, O. A. (2020). Fake NEws Detection Using Machine Learning Ensemble Methods. Complexity, 11 pages.

Dong-Ho, L., Yu-Ri, K., Hyeong-Jun, K., & Yu-Jun, Y. (2019). Feke News detection using Deep Learning . Journal of Information Processing Systems, 1119-1130.

Shalini, P., Sankeerthi, P., Subba, R. N., & Dinesh Acharya. (2022). Fake News Detection from Online media using Machine Learning Classifiers. 1st international Conference on Artificial Intelligence, Computational Electronics and Communication System (pp. 28-30). Manipal India: Journal of Physics: Conference Series.

Ali, H. H., & Heba, Y. A. (2022). Fake News Detection Based on the Machine Learning Model. Design Engineering, 1373-1378.

Haumahu, J. P., Silvester, D. H., & Yaddarabullah, Y. (2020). Fake news classification for Indonesian news using Extreme Gradient Boosting (XGBoost). The 5th Annual Applied Science and Engineering Conference. IOP Publishing .

Pritika, B., Preeti, S., & Raj, K. (2019). Fake News Detection using Bi-directional LSTM-Recurrent Neural Network. International Conference on recent trends in advanced computing 2019, ICRTAC 2019. India: Procedia Computer Science.

Zhibin, W., & Huatai, X. (2021). Performance comparison of different machine learning model in detecting fake news. Sweden: Open Access.

Kai, N., Sharon, L., & William, Y. W. (2020). Fakeddit. Retrieved from https://fakeddit.netlify.app/

William, Y. W. (2017). "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. arXiv.

Rowan, Z., Ari, H., Hannah, R., Yonatan, B., Ali, F., Franziska, R., & Yejin, C. (2020). Defending Against Neural Fake News. arXiv.

Kai, S., Deepak, M., Suhang, W., Dongwon, L., & Huan, L. (2018). FakeNewsNet: A data respository with news content, social context and dynamic information for studying fake news on social media . Journal of computer science.

Dean, P., & Delip, R. (2017, June 15). Fake News Challenge Stage 1 (FNC-1): Stance Detection. Retrieved from Fake News Challenge: httpp://www.fakenewschallenge.org/

Nguyen, V., & Kyumin, L. (2020). Where Are Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News. arXiv.

Eugenio, T., Gabriele, B., Marco, L. D., Stefano, M., & Luca, d. A. (2017). Some Like it Hoax: Automated Fake News Detection in Social Networks. arXiv.

Jeppe, N., Benjamin, D. H., & Sibel, A. (2019). NELA-GT 2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.

Mauricio, G., Benjamin, D. H., & Sibel, A. (2019). NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.

Maurico, G., Benjamin D, H., & Sibel, A. (2020). NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv.

Yingtong, D., Kai, S., Congying, X., Philip, S. Y., & Lichao, S. (2021). User Preference Aware Fake News Detection. arXiv.

Qiong, N., Juan, C., Yongchun, Z., Yanyan, W., & Jintao, L. (2022). MDFEND: Multi-domain Fake News Detection. arXiv.

Parth, P., Shivam, S., Srinivas, P., Vineeth, G., Gitanjali, K., Md, S. A., . . . Tanmoy, C. (2020). Fighting an Infodemic: Covid-19 Fake News Dataset. arXiv.

Yichuan, L., Bohan, J., Kia, S., & Huan, L. (2020). MM-COVID: A Multilingual and Multimodal Data Respository for Combating COVID-19 Disinformation. arXiv.

Mohamed, S. H., & Hassina, A. (2021). AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset. arXiv.

Zobaer, H., Ashraful, R., Saiful, I., & Sudipta, K. (2020). BanFakeNews: A Dataset for Detecting Fake News in Bangla. arXiv.

Jan, C. B., Julianne, A. T., & Charibeth, C. (2020). Localization of Fake News Detection via Multitask Transfer Learning. arXiv.


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162