Building a Fortress Against Fake News Harnessing the Power of Subfields in Artificial Intelligence
Main Article Content
Keywords
machine learning (ML), hybrid model, automated system, accuracy, fake news
Abstract
Given the prevalence of fake news in today’s tech-driven era, an urgent need exists for an automated mechanism to effectively curb its dissemination. This research aims to demonstrate the impacts of fake news through a literature review and establish a reliable system for identifying it using machine (ML) learning classifiers. By combining CNN, RNN, and ANN models, a novel model is proposed to detect fake news with 94.5% accuracy. Prior studies have successfully employed ML algorithms to identify false information by analysing textual and visual features in standard datasets. The comprehensive literature review emphasises the consequences of fake news on individuals, economies, societies, politics, and free expression. The proposed hybrid model, trained on extensive data and evaluated using accuracy, precision and recall measures, outperforms existing models. This study underscores the importance of developing automated systems to counter the spread of fake news and calls for further research in this domain.
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References
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Singhal, S., Kabra, A., Sharma, M., Shah, R. R., Chakraborty, T., & Kumaraguru, P. (2020). SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13915–13916. https://doi.org/10.1609/aaai.v34i10.7230
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Sullivan, M. C. (2019). Libraries and fake news: What’s the problem? What’s the plan?. Communications in Information Literacy, 13(1), 7. https://doi.org/10.15760/comminfolit.2019.13.1.7
Zafarani, R., Zhou, X., Shu, K., & Liu, H. (2019). Fake News Research. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3207–3208. https://doi.org/10.1145/3292500.3332287
Ahmad, T., Faisal, M. S., Rizwan, A., Alkanhel, R., Khan, P. W., & Muthanna, A. (2022). Efficient fake news detection mechanism using enhanced deep learning model. Applied Sciences, 12(3), 1743. https://doi.org/10.3390/app12031743
Almenar, E., Aran-Ramspott, S., Suau, J., & Masip, P. (2021). Gender differences in tackling fake news: Different degrees of concern, but same problems. Media and Communication, 9(1), 229–238. https://doi.org/10.17645/mac.v9i1.3523
Altheneyan, A., & Alhadlaq, A. (2023). Big data ML-based fake news detection using distributed learning. IEEE Access, 11, 29447–29463. https://doi.org/10.1109/ACCESS.2023.3260763
Aslam, N., Ullah Khan, I., Alotaibi, F. S., Aldaej, L. A., & Aldubaikil, A. K. (2021). Fake detect: A deep learning ensemble model for fake news detection. Complexity, 2021, 1–8. https://doi.org/10.1155/2021/5557784
Bago, B., Rand, D. G., & Pennycook, G. (2020). Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines. Journal of Experimental Psychology: General, 149(8), 1608. https://psycnet.apa.org/doi/10.1037/xge0000729
Bakir, V., & McStay, A. (2018). Fake news and the economy of emotions: Problems, causes, solutions. Digital Journalism, 6(2), 154–175. https://doi.org/10.1080/21670811.2017.1345645
Bhatt, G., Sharma, A., Sharma, S., Nagpal, A., Raman, B., & Mittal, A. (2018). Combining Neural, Statistical and External Features for Fake News Stance Identification. WWW ’18: Companion Proceedings of The Web Conference 2018. https://doi.org/10.1145/3184558.3191577
Butler, A. (2018). Protecting the Democratic Role of the Press: A Legal Solution to Fake News. Washington University Law Review, 96(2), 419–440.
Creech, B. (2020). Fake news and the discursive construction of technology companies’ social power. Media, Culture & Society, 42(6), 952–968. https://doi.org/10.1177/0163443719899801
De Oliveira, D. V. B., & Albuquerque, U. P. (2021). Cultural evolution and digital media: Diffusion of fake news about COVID-19 on Twitter. SN Computer Science, 2(6), 1–12. https://doi.org/10.1007/s42979-021-00836-w
Ghosh, S., & Shah, C. (2018). Towards automatic fake news classification. Proceedings of the Association for Information Science and Technology, 55(1), 805–807. https://doi.org/10.1002/pra2.2018.14505501125
Ho, K. K., Chan, J. Y., & Chiu, D. K. (2022). Fake news and misinformation during the pandemic: What we know and what we do not know. IT Professional, 24(2), 19–24. https://doi.org/10.1109/MITP.2022.3142814
Khan, A., Brohman, K., & Addas, S. (2022). The anatomy of ‘fake news’: Studying false messages as digital objects. Journal of Information Technology, 37(2), 122–143. https://doi.org/10.1177/02683962211037693
Kokiantonis, A. (2022) News.csv. https://www.kaggle.com/datasets/antonioskokiantonis/newscsv/code. Accessed on 26 April 2023.
Lakshmanan, L. V., Simpson, M., & Thirumuruganathan, S. (2019). Combating fake news: a data management and mining perspective. Proceedings of the VLDB Endowment, 12(12), 1990–1993. https://doi.org/10.14778/3352063.3352117
Leeder, C. (2019). How college students evaluate and share “fake news” stories. Library & Information Science Research, 41(3), 100967. https://doi.org/10.1016/j.lisr.2019.100967
Mahmoud, A. (2022) News.csv. https://www.kaggle.com/code/ahmedxmahmoud/fake-news-detection/input. Accessed on 2 April 2023.
Meel, P., & Vishwakarma, D. K. (2021). A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles. Expert Systems with Applications, 177, 115002. https://doi.org/10.1016/j.eswa.2021.115002
Naeem, S. B., Bhatti, R., & Khan, A. (2020). An exploration of how fake news is taking over social media and putting public health at risk. Health Information & Libraries Journal, 38(2), 143–149. https://doi.org/10.1111/hir.12320
Pearson, M. (2017). Teaching Media Law in a Post-truth Context: Strategies for Enhancing Learning about the Legal Risks of Fake News and Alternative Facts. Asia Pacific Media Educator, 27(1), 17–26. https://doi.org/10.1177/1326365x17704289
Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2017). Automatic detection of fake news. https://doi.org/10.48550/arXiv.1708.07104
Rawat, G., Pandey, T., Singh, T., Yadav, S., & Aggarwal, P. K. (2023, January). Fake news detection using machine learning. 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), pp. 759–762, IEEE. https://doi.org/10.1109/AISC56616.2023.10085488
Sharma, D. K., Singh, B., Agarwal, S., Pachauri, N., Alhussan, A. A., & Abdallah, H. A. (2023). Sarcasm detection over social media platforms using hybrid ensemble model with fuzzy logic. Electronics, 12(4), 937. https://doi.org/10.3390/electronics12040937
Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 312–320. https://doi.org/10.1145/3289600.3290994
Singhal, S., Shah, R. R., Chakraborty, T., Kumaraguru, P., & Satoh, S. (2019). SpotFake: A Multi-modal Framework for Fake News Detection. IEEE International Conference on Multimedia Big Data (BigMM), pp. 39–47. https://doi.org/10.1109/BigMM.2019.00-44
Singhal, S., Kabra, A., Sharma, M., Shah, R. R., Chakraborty, T., & Kumaraguru, P. (2020). SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13915–13916. https://doi.org/10.1609/aaai.v34i10.7230
Stewart, E. (2021). Detecting Fake News: Two Problems for Content Moderation. Philosophy & Technology, 34, 923–940. https://doi.org/10.1007/s13347-021-00442-x
Sullivan, M. C. (2019). Libraries and fake news: What’s the problem? What’s the plan?. Communications in Information Literacy, 13(1), 7. https://doi.org/10.15760/comminfolit.2019.13.1.7
Zafarani, R., Zhou, X., Shu, K., & Liu, H. (2019). Fake News Research. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3207–3208. https://doi.org/10.1145/3292500.3332287
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Published
Sep 30, 2023
Article Details
How to Cite
Fahad, N., Goh, K. O. M., Hossen, M. I., Tee, C., & Ali, M. A. (2023). Building a Fortress Against Fake News: Harnessing the Power of Subfields in Artificial Intelligence. Journal of Telecommunications and the Digital Economy, 11(3), 68–83. https://doi.org/10.18080/jtde.v11n3.765
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Telecommunications
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