{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:01:29Z","timestamp":1780074089532,"version":"3.54.0"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T00:00:00Z","timestamp":1613520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.<\/jats:p>","DOI":"10.3390\/computation9020020","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T01:47:33Z","timestamp":1613526453000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Deep Learning for Fake News Detection in a Pairwise Textual Input Schema"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2844-5488","authenticated-orcid":false,"given":"Despoina","family":"Mouratidis","sequence":"first","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-8821","authenticated-orcid":false,"given":"Maria Nefeli","family":"Nikiforos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3270-5078","authenticated-orcid":false,"given":"Katia Lida","family":"Kermanidis","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nikiforos, M.N., Vergis, S., Stylidou, A., Augoustis, N., Kermanidis, K.L., and Maragoudakis, M. (2020). Fake News Detection Regarding the Hong Kong Events from Tweets. IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer.","DOI":"10.1007\/978-3-030-49190-1_16"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_3","unstructured":"Victor, U. (2020). Robust Semi-Supervised Learning for Fake News Detection. [Ph.D Thesis, Prairie View A&M University]."},{"key":"ref_4","first-page":"11572","article-title":"A Survey on Recent Advances in Machine Learning Techniques for Fake News Detection","volume":"83","author":"Merryton","year":"2020","journal-title":"Test Eng. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Agarwal, A., and Dixit, A. (2020, January 13\u201315). Fake News Detection: An Ensemble Learning Approach. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121030"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., Deng, Q., and Gao, J. (2020, January 7\u201312). Weak supervision for fake news detection via reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5389"},{"key":"ref_7","unstructured":"Dong, X., Victor, U., Chowdhury, S., and Qian, L. (2019). Deep Two-path Semi-supervised Learning for Fake News Detection. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vishwakarma, D.K., and Jain, C. (2020, January 5\u20137). Recent State-of-the-art of Fake News Detection: A Review. Proceedings of the 2020 International Conference for Emerging Technology (INCET), Belgaum, India.","DOI":"10.1109\/INCET49848.2020.9153985"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1080\/01292986.2020.1825757","article-title":"Chatting in a mobile chamber: Effects of instant messenger use on tolerance toward political misinformation among South Koreans","volume":"30","author":"Gill","year":"2020","journal-title":"Asian J. Commun."},{"key":"ref_10","unstructured":"Koirala, A. (2020). COVID-19 Fake News Classification with Deep Learning. Preprint."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5539\/ijel.v11n1p99","article-title":"Linguistic-Based Detection of Fake News in Social Media","volume":"11","author":"Mahyoob","year":"2020","journal-title":"Forthcom. Int. J. Engl. Linguist."},{"key":"ref_12","unstructured":"Alam, S., and Ravshanbekov, A. (2019). Sieving Fake News From Genuine: A Synopsis. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, W., and Mehta, V. (2019, January 11\u201312). Fake News Detection in Social Networks Using Machine Learning and Deep Learning: Performance Evaluation. Proceedings of the 2019 IEEE International Conference on Industrial Internet (ICII), Orlando, FL, USA.","DOI":"10.1109\/ICII.2019.00070"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Agrawal, T., Gupta, R., and Narayanan, S. (September, January 28). Multimodal detection of fake social media use through a fusion of classification and pairwise ranking systems. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081367"},{"key":"ref_15","first-page":"209","article-title":"Fake News Classification Bimodal using Convolutional Neural Network and Long Short-Term Memory","volume":"11","author":"Abdullah","year":"2020","journal-title":"Int. J. Emerg. Technol. Learn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e3767","DOI":"10.1002\/ett.3767","article-title":"Fake news detection using deep learning models: A novel approach","volume":"31","author":"Kumar","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.procs.2020.01.072","article-title":"Fake News Detection using Bi-directional LSTM-Recurrent Neural Network","volume":"165","author":"Bahad","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zervopoulos, A., Alvanou, A.G., Bezas, K., Papamichail, A., Maragoudakis, M., and Kermanidis, K. (2020). Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter. IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer.","DOI":"10.1007\/978-3-030-49186-4_34"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"213154","DOI":"10.1109\/ACCESS.2020.3040604","article-title":"Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection","volume":"8","author":"Sansonetti","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jeronimo, C.L.M., Marinho, L.B., Campelo, C.E., Veloso, A., and da Costa Melo, A.S. (2019, January 2\u20134). Fake News Classification Based on Subjective Language. Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, Munich, Germany.","DOI":"10.1145\/3366030.3366039"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Alves, J.L., Weitzel, L., Quaresma, P., Cardoso, C.E., and Cunha, L. (2019). Brazilian Presidential Elections in the Era of Misinformation: A Machine Learning Approach to Analyse Fake News. Iberoamerican Congress on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-030-33904-3_7"},{"key":"ref_22","unstructured":"Perera, K. (2020). The Misinformation Era: Review on Deep Learning Approach to Fake News Detection. Preprint."},{"key":"ref_23","first-page":"114","article-title":"Linguistic feature based learning model for fake news detection and classification","volume":"169","author":"Anshika","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De Oliveira, N.R., Pisa, P.S., Lopez, M.A., de Medeiros, D.S.V., and Mattos, D.M.F. (2021). Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information, 12.","DOI":"10.3390\/info12010038"},{"key":"ref_25","first-page":"106","article-title":"Multiple features based approach for automatic fake news detection on social networks using deep learning","volume":"100","author":"Ranjan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201cliar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"key":"ref_28","unstructured":"Ruchansky, N., Seo, S., and Liu, Y. (2017, January 6\u201310). Csi: A hybrid deep model for fake news detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"K\u00f6ppel, M., Segner, A., Wagener, M., Pensel, L., Karwath, A., and Kramer, S. (2019). Pairwise learning to rank by neural networks revisited: Reconstruction, theoretical analysis and practical performance. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-030-46133-1_15"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mouratidis, D., Kermanidis, K.L., and Sosoni, V. (2020). Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation. IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer.","DOI":"10.1007\/978-3-030-49186-4_7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Augenstein, I., Ruder, S., and S\u00f8gaard, A. (2018). Multi-task learning of pairwise sequence classification tasks over disparate label spaces. arXiv.","DOI":"10.18653\/v1\/N18-1172"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_33","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","unstructured":"Keras, K. (2021, January 31). Deep Learning Library for Theano and Tensorflow. Available online: https:\/\/keras.io\/."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","first-page":"40","article-title":"The combined use of the wiener polynomial and SVM for material classification task in medical implants production","volume":"10","author":"Izonin","year":"2018","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. 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