Enhanced Propaganda Detection in Public Social Media Discussions Using a Fine-Tuned Deep Learning Model: A Diffusion of Innovation Perspective
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| Publicado no: | Future Internet vol. 17, no. 5 (2025), p. 212 |
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| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Ahmad Pir Noman |u IRC for Finance and Digital Economy, KFUPM Business School, King Fahad University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; ahmadpir40@gmail.com | |
| 245 | 1 | |a Enhanced Propaganda Detection in Public Social Media Discussions Using a Fine-Tuned Deep Learning Model: A Diffusion of Innovation Perspective | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a During the COVID-19 pandemic, social media platforms emerged as both vital information sources and conduits for the rapid spread of propaganda and misinformation. However, existing studies often rely on single-label classification, lack contextual sensitivity, or use models that struggle to effectively capture nuanced propaganda cues across multiple categories. These limitations hinder the development of robust, generalizable detection systems in dynamic online environments. In this study, we propose a novel deep learning (DL) framework grounded in fine-tuning the RoBERTa model for a multi-label, multi-class (ML-MC) classification task, selecting RoBERTa due to its strong contextual representation capabilities and demonstrated superiority in complex NLP tasks. Our approach is rigorously benchmarked against traditional and neural methods, including, TF-IDF with n-grams, Conditional Random Fields (CRFs), and long short-term memory (LSTM) networks. While LSTM models show strong performance in capturing sequential patterns, our RoBERTa-based model achieves the highest overall accuracy at 88%, outperforming state-of-the-art baselines. Framed within the diffusion of innovations theory, the proposed model offers clear relative advantages—including accuracy, scalability, and contextual adaptability—that support its early adoption by Information Systems researchers and practitioners. This study not only contributes a high-performing detection model but also delivers methodological and theoretical insights for combating propaganda in digital discourse, enhancing resilience in online information ecosystems. | |
| 653 | |a Innovations | ||
| 653 | |a Accuracy | ||
| 653 | |a Labels | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Information systems | ||
| 653 | |a Information sources | ||
| 653 | |a Social networks | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Neural networks | ||
| 653 | |a Task complexity | ||
| 653 | |a False information | ||
| 653 | |a Deep learning | ||
| 653 | |a Digital media | ||
| 653 | |a COVID-19 | ||
| 700 | 1 | |a Shah, Adnan Muhammad |u Chair of Marketing and Innovation, Department of Socioeconomics, University of Hamburg, 20146 Hamburg, Germany; adnan.shah@uni-hamburg.de | |
| 700 | 1 | |a Lee, KangYoon |u Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea | |
| 773 | 0 | |t Future Internet |g vol. 17, no. 5 (2025), p. 212 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211965215/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211965215/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211965215/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |