Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
2. EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Abstract
Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference89 articles.
1. Ahmed, S., Hinkelmann, K., and Corradini, F. (2022). Combating Fake News with Computational Intelligence Techniques, Springer.
2. Hao, F., Yang, Y., Shang, J., and Park, D.-S. (2023). IEEE Transactions on Computational Social Systems, IEEE.
3. Propaganda Detection And Challenges Managing Smart Cities Information On Social Media;Ahmad;EAI Endorsed Trans. Smart Cities,2023
4. Khanday, A.M.U.D., Wani, M.A., Rabani, S.T., and Khan, Q.R. (2023). Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks. Sustainability, 15.
5. Proppy: Organizing the News Based on Their Propagandistic Content;Jaradat;Inf. Process. Manag.,2019
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