{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:30:56Z","timestamp":1791873056866,"version":"4.3.4"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62166042"],"award-info":[{"award-number":["No. 62166042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Xinjiang ,China","award":["2021D01C076"],"award-info":[{"award-number":["2021D01C076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the existing false information detection methods, the quality of the extracted single-modality features is low, the information between different modalities cannot be fully fused, and the original information will be lost when the information of different modalities is fused. This paper proposes a false information detection via multimodal feature fusion and multi-classifier hybrid prediction. In this method, first, bidirectional encoder representations for transformers are used to extract the text features, and S win-transformer is used to extract the picture features, and then, the trained deep autoencoder is used as an early fusion method of multimodal features to fuse text features and visual features, and the low-dimensional features are taken as the joint features of the multimodalities. The original features of each modality are concatenated into the joint features to reduce the loss of original information. Finally, the text features, image features and joint features are processed by three classifiers to obtain three probability distributions, and the three probability distributions are added proportionally to obtain the final prediction result. Compared with the attention-based multimodal factorized bilinear pooling, the model achieves 4.3% and 1.2% improvement in accuracy on Weibo dataset and Twitter dataset. The experimental results show that the proposed model can effectively integrate multimodal information and improve the accuracy of false information detection.<\/jats:p>","DOI":"10.3390\/a15040119","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:40:14Z","timestamp":1648590014000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Yi","family":"Liang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1639-8899","authenticated-orcid":false,"given":"Turdi","family":"Tohti","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-308X","authenticated-orcid":false,"given":"Askar","family":"Hamdulla","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","first-page":"100007","article-title":"Fake news detection: A hybrid CNN-RNN based deep learning approach","volume":"1","author":"Nasir","year":"2021","journal-title":"Int. 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