Hierarchical graph-based integration network for propaganda detection in textual news articles on social media
Abstract During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs),...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-74126-9 |
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| author | Pir Noman Ahmad Jiequn Guo Nagwa M. AboElenein Qazi Mazhar ul Haq Sadique Ahmad Abeer D. Algarni Abdelhamied A. Ateya |
| author_facet | Pir Noman Ahmad Jiequn Guo Nagwa M. AboElenein Qazi Mazhar ul Haq Sadique Ahmad Abeer D. Algarni Abdelhamied A. Ateya |
| author_sort | Pir Noman Ahmad |
| collection | DOAJ |
| description | Abstract During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data. In this study, we propose a Hierarchical Graph-based Integration Network (H-GIN) designed for detecting propaganda in text within a defined domain using multilabel classification. H-GIN is extracted to build a bi-layer graph inter-intra-channel, such as Residual-driven Enhancement and Processing (RDEP) and Attention-driven Multichannel feature Fusing (ADMF) with suitable labels at two distinct classification levels. First, RDEP procedures facilitate information interactions between distant nodes. Second, by employing these guidelines, ADMF standardizes the Tri-Channels 3-S (sequence, semantic, and syntactic) layer, enabling effective propaganda detection through related and unrelated information propagation of news representations into a classifier from the existing ProText, Qprop, and PTC datasets, thereby ensuring its availability to the public. The H-GIN model demonstrated exceptional performance, achieving an impressive 82% accuracy and surpassing current leading models. Notably, the model’s capacity to identify previously unseen examples across diverse openness scenarios at 82% accuracy using the ProText dataset was particularly significant. |
| format | Article |
| id | doaj-art-3f57d6b88c1042d198a03a20cf5b2068 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3f57d6b88c1042d198a03a20cf5b20682025-01-19T12:20:52ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-74126-9Hierarchical graph-based integration network for propaganda detection in textual news articles on social mediaPir Noman Ahmad0Jiequn Guo1Nagwa M. AboElenein2Qazi Mazhar ul Haq3Sadique Ahmad4Abeer D. Algarni5Abdelhamied A. Ateya6Ningbo China Institute for Supply Chain Innovation, MIT Global SCALE NetworkNingbo China Institute for Supply Chain Innovation, MIT Global SCALE NetworkFaculty of Computers and Information, Menoufia UniversityIntelligent Computing Lab, Department of Computer Science and Engineering, International Bachelors Program in Informatics Yuan Ze UniversityEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan UniversityAbstract During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data. In this study, we propose a Hierarchical Graph-based Integration Network (H-GIN) designed for detecting propaganda in text within a defined domain using multilabel classification. H-GIN is extracted to build a bi-layer graph inter-intra-channel, such as Residual-driven Enhancement and Processing (RDEP) and Attention-driven Multichannel feature Fusing (ADMF) with suitable labels at two distinct classification levels. First, RDEP procedures facilitate information interactions between distant nodes. Second, by employing these guidelines, ADMF standardizes the Tri-Channels 3-S (sequence, semantic, and syntactic) layer, enabling effective propaganda detection through related and unrelated information propagation of news representations into a classifier from the existing ProText, Qprop, and PTC datasets, thereby ensuring its availability to the public. The H-GIN model demonstrated exceptional performance, achieving an impressive 82% accuracy and surpassing current leading models. Notably, the model’s capacity to identify previously unseen examples across diverse openness scenarios at 82% accuracy using the ProText dataset was particularly significant.https://doi.org/10.1038/s41598-024-74126-9 |
| spellingShingle | Pir Noman Ahmad Jiequn Guo Nagwa M. AboElenein Qazi Mazhar ul Haq Sadique Ahmad Abeer D. Algarni Abdelhamied A. Ateya Hierarchical graph-based integration network for propaganda detection in textual news articles on social media Scientific Reports |
| title | Hierarchical graph-based integration network for propaganda detection in textual news articles on social media |
| title_full | Hierarchical graph-based integration network for propaganda detection in textual news articles on social media |
| title_fullStr | Hierarchical graph-based integration network for propaganda detection in textual news articles on social media |
| title_full_unstemmed | Hierarchical graph-based integration network for propaganda detection in textual news articles on social media |
| title_short | Hierarchical graph-based integration network for propaganda detection in textual news articles on social media |
| title_sort | hierarchical graph based integration network for propaganda detection in textual news articles on social media |
| url | https://doi.org/10.1038/s41598-024-74126-9 |
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