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Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Author

Listed:
  • Pir Noman Ahmad

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Yuanchao Liu

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Gauhar Ali

    (EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Mudasir Ahmad Wani

    (EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Mohammed ElAffendi

    (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.

Suggested Citation

  • Pir Noman Ahmad & Yuanchao Liu & Gauhar Ali & Mudasir Ahmad Wani & Mohammed ElAffendi, 2023. "Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data," Mathematics, MDPI, vol. 11(12), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2668-:d:1169221
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    References listed on IDEAS

    as
    1. Akib Mohi Ud Din Khanday & Mudasir Ahmad Wani & Syed Tanzeel Rabani & Qamar Rayees Khan, 2023. "Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
    2. Na Guo & Yaqi Wang & Haonan Jiang & Xiufeng Xia & Yu Gu, 2022. "TALI: An Update-Distribution-Aware Learned Index for Social Media Data," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    3. Yanmei Hu & Bo Yang & Bin Duo & Xing Zhu, 2022. "Exhaustive Exploitation of Local Seeding Algorithms for Community Detection in a Unified Manner," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
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