{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:13:28Z","timestamp":1778818408807,"version":"3.51.4"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T00:00:00Z","timestamp":1674259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia","award":["PNURSP2023R300"],"award-info":[{"award-number":["PNURSP2023R300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models.<\/jats:p>","DOI":"10.3390\/sym15020296","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T03:21:54Z","timestamp":1674616914000},"page":"296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["HyproBert: A Fake News Detection Model Based on Deep Hypercontext"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1536-2285","authenticated-orcid":false,"given":"Muhammad Imran","family":"Nadeem","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5810-4983","authenticated-orcid":false,"given":"Syed Agha Hassnain","family":"Mohsan","sequence":"additional","affiliation":[{"name":"Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6210-4487","authenticated-orcid":false,"given":"Kanwal","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Shafiq","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faten Khalid","family":"Karim","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-5898","authenticated-orcid":false,"given":"Samih M.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kumar, S., West, R., and Leskovec, J. (2016, January 11\u201315). Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes. Proceedings of the 25th international conference on world wide web, Montreal, Canada.","DOI":"10.1145\/2872427.2883085"},{"key":"ref_2","unstructured":"Ratkiewicz, J., Conover, M., Meiss, M., Gon\u00e7alves, B., Patil, S., Flammini, A., and Menczer, F. (April, January 28). 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