{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:23:37Z","timestamp":1774967017903,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["DMS-2022448"],"award-info":[{"award-number":["DMS-2022448"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The proliferation of fake news across multiple modalities has emerged as a critical challenge in the modern information landscape, necessitating advanced detection methods. This study proposes a comprehensive framework for fake news detection integrating text, images, and videos using machine learning and deep learning techniques. The research employs a dual-phased methodology, first analyzing textual data using various classifiers, then developing a multimodal approach combining BERT for text analysis and a modified CNN for visual data. Experiments on the ISOT fake news dataset and MediaEval 2016 image verification corpus demonstrate the effectiveness of the proposed models. For textual data, the Random Forest classifier achieved 99% accuracy, outperforming other algorithms. The multimodal approach showed superior performance compared to baseline models, with a 3.1% accuracy improvement over existing multimodal techniques. This research contributes to the ongoing efforts to combat misinformation by providing a robust, adaptable framework for detecting fake news across different media formats, addressing the complexities of modern information dissemination and manipulation.<\/jats:p>","DOI":"10.3390\/s24186062","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T09:23:04Z","timestamp":1726737784000},"page":"6062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Ensemble Techniques for Robust Fake News Detection: Integrating Transformers, Natural Language Processing, and Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5486-0415","authenticated-orcid":false,"given":"Mohammed","family":"Al-alshaqi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3638-3464","authenticated-orcid":false,"given":"Danda B.","family":"Rawat","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4445-2192","authenticated-orcid":false,"given":"Chunmei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"ref_1","unstructured":"Han, S. (2022). Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Duc Tuan, N.M., and Minh, P. (2021, January 2\u20134). Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection. Proceedings of the 2021 RIVF International Conference on Computing and Communication Technologies, RIVF 2021, Hanoi, Vietnam.","DOI":"10.1109\/RIVF51545.2021.9642125"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","article-title":"FakeBERT: Fake news detection in social media with a BERT-based deep learning approach","volume":"80","author":"Kaliyar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Alshaqi, M., Rawat, D.B., and Liu, C. (2023, January 24). Emotion-Aware Fake News Detection on Social Media with BERT Embeddings. Proceedings of the 2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), Karawang, Indonesia,.","DOI":"10.1109\/ICMERALDA60125.2023.10458214"},{"key":"ref_5","unstructured":"Sarkar, S., Yang, F., and Mukherjee, A. (2018, January 20\u201326). Attending sentences to detect satirical fake news. Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, NM, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information credibility on Twitter. Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, Hyderabad, India.","DOI":"10.1145\/1963405.1963500"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Al-Alshaqi, M., and Rawat, D.B. (2023, January 21\u201324). Using Attention-Based Models to Automate Fake News Detection. Proceedings of the 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/SNAMS60348.2023.10375408"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154704","DOI":"10.1109\/ACCESS.2021.3128742","article-title":"BERT, XLNet or RoBERTa: The Best Transfer Learning Model to Detect Clickbaits","volume":"9","author":"Rajapaksha","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huy, N.Q., Duc Tuan, N.M., Duong, N.M., and Minh, P. (2021, January 10\u201312). AimeLaw at ALQAC 2021: Enriching Neural Network Models with Legal-Domain Knowledge. Proceedings of the 2021 13th International Conference on Knowledge and Systems Engineering (KSE), Bangkok, Thailand.","DOI":"10.1109\/KSE53942.2021.9648636"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9029","DOI":"10.1007\/s11042-022-12788-1","article-title":"A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers","volume":"82","author":"Choudhury","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shah, P., and Kobti, Z. (2020, January 19\u201324). Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185643"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/asi.24359","article-title":"Detecting fake news stories via multimodal analysis","volume":"72","author":"Singh","year":"2021","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"132363","DOI":"10.1109\/ACCESS.2021.3114093","article-title":"Multi-Level Multi-Modal Cross-Attention Network for Fake News Detection","volume":"9","author":"Ying","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102437","DOI":"10.1016\/j.ipm.2020.102437","article-title":"A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks","volume":"58","author":"Song","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., and Shang, L. (2022, January 25\u201329). Cross-modal Ambiguity Learning for Multimodal Fake News Detection. Proceedings of the ACM Web Conference 2022, Lyon, France.","DOI":"10.1145\/3485447.3511968"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qian, S., Wang, J., Hu, J., Fang, Q., and Xu, C. (2021, January 11\u201315). Hierarchical Multi-modal Contextual Attention Network for Fake News Detection. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021, Virtual.","DOI":"10.1145\/3404835.3462871"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8132","DOI":"10.1007\/s10489-021-02345-y","article-title":"ConvNet frameworks for multi-modal fake news detection","volume":"51","author":"Raj","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.patrec.2022.01.007","article-title":"Effective fake news video detection using domain knowledge and multimodal data fusion on YouTube","volume":"154","author":"Choi","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, M., Chu, X., and Subbalakshmi, K.P. (2021, January 8\u201311). MMCoVaR: Multimodal COVID-19 vaccine focused data repository for fake news detection and a baseline architecture for classification. Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, Virtual.","DOI":"10.1145\/3487351.3488346"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102610","DOI":"10.1016\/j.ipm.2021.102610","article-title":"Detecting fake news by exploring the consistency of multimodal data","volume":"58","author":"Xue","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103193","DOI":"10.1016\/j.ipm.2022.103193","article-title":"Joint multimodal sentiment analysis based on information relevance","volume":"60","author":"Chen","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qi, P., Cao, J., Li, R., and Liu, H. (2021, January 20\u201324). Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues. Proceedings of the 29th ACM International Conference on Multimedia, MM 2021, Virtual.","DOI":"10.1145\/3474085.3481548"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., and Satoh, S. (2019, January 1\u201313). SpotFake: A multi-modal framework for fake news detection. Proceedings of the 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019, Singapore.","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Prakash, K.B. (2022). Fake News Detection. Data Science Handbook, Scrivener Publishing LLC.","DOI":"10.1002\/9781119858010.ch18"},{"key":"ref_25","unstructured":"(2023, December 12). MKLab-ITI. Available online: https:\/\/github.com\/MKLab-ITI\/image-verification-corpus\/tree\/master."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6062\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:59:41Z","timestamp":1760111981000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":25,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186062"],"URL":"https:\/\/doi.org\/10.3390\/s24186062","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]}}}