{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:19:23Z","timestamp":1780553963110,"version":"3.54.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Fake news spread in various areas has a major negative impact on social life. Meanwhile, fake news with text and visual content is more compelling than text-only content and quickly spreads across social media. Therefore, detecting fake news is a pressing task for the current society.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Concern the problem of extracting insufficient features, and the inability to merge multi-modality features effectively in detecting fake news. In this article, we propose a method for detecting fake news by fusing text and visual data. Firstly, we use two-branch to learn hidden layer information of modality to obtain more helpful features. Then we proposed a multimodal bilinear pooling mechanism to better merge textual and visual features and an attention mechanism to capture multimodal internal relationships for the detection of fake news.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>The experimental results demonstrated that our methodology outperformed the current state-of-the-art methodology on publicly accessible Weibo and Twitter datasets.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcomp.2023.1159063","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T04:30:48Z","timestamp":1682051448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["A two-branch multimodal fake news detection model based on multimodal bilinear pooling and attention mechanism"],"prefix":"10.3389","volume":"5","author":[{"given":"Ying","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"B1","first-page":"2425","article-title":"\u201cVqa: visual question answering,\u201d","author":"Antol","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.0473","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv preprint"},{"key":"B3","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. 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