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Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is to identify recent state-of-the-art deep learning methods used to detect fake news in social networks. This article presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities: unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact-checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.<\/jats:p>","DOI":"10.1145\/3700748","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T11:20:52Z","timestamp":1729682452000},"page":"1-50","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Modality Deep-learning Frameworks for Fake News Detection on Social Networks: A Systematic Literature Review"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0507-6955","authenticated-orcid":false,"given":"Mohamed","family":"Mostafa","sequence":"first","affiliation":[{"name":"Computer Science, King Saud University College of Computer and Information Sciences, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8253-9709","authenticated-orcid":false,"given":"Ahmad S","family":"Almogren","sequence":"additional","affiliation":[{"name":"Computer Science, King Saud University College of Computer and Information Sciences, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-7325","authenticated-orcid":false,"given":"Muhammad","family":"Al-Qurishi","sequence":"additional","affiliation":[{"name":"Research Department, Elm Company, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9244-8341","authenticated-orcid":false,"given":"Majed","family":"Alrubaian","sequence":"additional","affiliation":[{"name":"Information Systems, King Saud University College of Computer and Information Sciences, Riyadh, Saudi Arabia"}]}],"member":"320","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2022.2130248"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3217804.3217917"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2656635"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2753202"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4276"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2999033"},{"key":"e_1_3_2_8_2","first-page":"4396","volume-title":"10th International Conference on Language Resources and Evaluation (LREC\u201916)","author":"Zaatari Ayman Al","year":"2016","unstructured":"Ayman Al Zaatari, Rim El Ballouli, Shady ELbassouni, Wassim El-Hajj, Hazem Hajj, Khaled Shaban, Nizar Habash, and Emad Yahya. 2016. 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Derivation Of New Readability Formulas (Automated Readability Index Fog Count And Flesch Reading Ease Formula) For Navy Enlisted Personnel. Institute for Simulation and Training. 56. https:\/\/stars.library.ucf.edu\/istlibrary\/56","DOI":"10.21236\/ADA006655"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/CBMI50038.2021.9461898"},{"issue":"2004","key":"e_1_3_2_65_2","first-page":"1","article-title":"Procedures for performing systematic reviews","volume":"33","author":"Kitchenham Barbara","year":"2004","unstructured":"Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele Univ. 33, 2004 (2004), 1\u201326.","journal-title":"Keele, UK, Keele Univ."},{"key":"e_1_3_2_66_2","unstructured":"Elena Kochkina Maria Liakata and Arkaitz Zubiaga. 2018. PHEME dataset for rumour detection and veracity classification. Retrieved from https:\/\/figshare.com\/articles\/dataset\/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification\/6392078"},{"key":"e_1_3_2_67_2","article-title":"False information on web and social media: A survey","author":"Kumar Srijan","year":"2018","unstructured":"Srijan Kumar and Neil Shah. 2018. False information on web and social media: A survey. arXiv preprint arXiv:1804.08559 (2018).","journal-title":"arXiv preprint arXiv:1804.08559"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115412"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aao2998"},{"key":"e_1_3_2_70_2","article-title":"Detecting misinformation with LLM-predicted credibility signals and weak supervision","author":"Leite Jo\u00e3o A.","year":"2023","unstructured":"Jo\u00e3o A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, and Carolina Scarton. 2023. Detecting misinformation with LLM-predicted credibility signals and weak supervision. arXiv preprint arXiv:2309.07601 (2023).","journal-title":"arXiv preprint arXiv:2309.07601"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.11.314"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-019-01289-y"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/2897350.2897352"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17189-5_4"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806651"},{"key":"e_1_3_2_76_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. 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GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv preprint arXiv:2004.11648 (2020).","journal-title":"arXiv preprint arXiv:2004.11648"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103414"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36657-4_2"},{"key":"e_1_3_2_81_2","article-title":"A survey on computational propaganda detection","author":"Martino Giovanni Da San","year":"2020","unstructured":"Giovanni Da San Martino, Stefano Cresci, Alberto Barr\u00f3n-Cede\u00f1o, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2020. A survey on computational propaganda detection. arXiv preprint arXiv:2007.08024 (2020).","journal-title":"arXiv preprint arXiv:2007.08024"},{"issue":"8","key":"e_1_3_2_82_2","first-page":"639","article-title":"SMOG grading-a new readability formula","volume":"12","author":"McLaughlin G. Harry","year":"1969","unstructured":"G. Harry McLaughlin. 1969. SMOG grading-a new readability formula. J. Read. 12, 8 (1969), 639\u2013646.","journal-title":"J. Read."},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112986"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00738-y"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.24052\/BMR\/V13NU01\/ART-13"},{"key":"e_1_3_2_86_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).","journal-title":"arXiv preprint arXiv:1301.3781"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-019-00576-5"},{"key":"e_1_3_2_88_2","first-page":"6149","volume-title":"12th Language Resources and Evaluation Conference","author":"Nakamura Kai","year":"2020","unstructured":"Kai Nakamura, Sharon Levy, and William Yang Wang. 2020. Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. In 12th Language Resources and Evaluation Conference. 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A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638 (2017).","journal-title":"arXiv preprint arXiv:1702.05638"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00746-y"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00062"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462871"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-15777-6_26"},{"key":"e_1_3_2_109_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018) 1--12. https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02345-y"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/GCAT52182.2021.9587698"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121641"},{"key":"e_1_3_2_113_2","article-title":"Dynamic routing between capsules","volume":"30","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. Advan. Neural Inf. Process. Syst. 30 (2017).","journal-title":"Advan. Neural Inf. Process. 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Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).","journal-title":"arXiv preprint arXiv:1409.1556"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06086-4"},{"key":"e_1_3_2_126_2","volume-title":"Automated Readability Index","author":"Smith Edgar A.","year":"1967","unstructured":"Edgar A. Smith and R. J. Senter. 1967. Automated Readability Index. Vol. 66. Aerospace Medical Research Laboratories, Aerospace Medical Division, Air Force Systems Command."},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.077"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102437"},{"key":"e_1_3_2_129_2","article-title":"Fake news detectors are biased against texts generated by large language models","author":"Su Jinyan","year":"2023","unstructured":"Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov. 2023. Fake news detectors are biased against texts generated by large language models. arXiv preprint arXiv:2309.08674 (2023).","journal-title":"arXiv preprint arXiv:2309.08674"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119183518.ch4"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119183518.ch2"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119183518"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119183518.ch1"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_135_2","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019).","journal-title":"arXiv preprint arXiv:1905.11946"},{"key":"e_1_3_2_136_2","article-title":"The science of detecting LLM-generated texts","author":"Tang Ruixiang","year":"2023","unstructured":"Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023. The science of detecting LLM-generated texts. arXiv preprint arXiv:2303.07205 (2023).","journal-title":"arXiv preprint arXiv:2303.07205"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSP48568.2020.9182398"},{"key":"e_1_3_2_138_2","article-title":"FEVER: A large-scale dataset for fact extraction and VERification","author":"Thorne James","year":"2018","unstructured":"James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: A large-scale dataset for fact extraction and VERification. arXiv preprint arXiv:1803.05355 (2018).","journal-title":"arXiv preprint arXiv:1803.05355"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1109\/NSWCTC.2010.66"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICOSC.2019.8665593"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524664"},{"key":"e_1_3_2_142_2","first-page":"1","volume-title":"RIVF International Conference on Computing and Communication Technologies (RIVF\u201921)","author":"Tuan Nguyen Manh Duc","year":"2021","unstructured":"Nguyen Manh Duc Tuan and Pham Quang Nhat Minh. 2021. Multimodal fusion with BERT and attention mechanism for fake news detection. In RIVF International Conference on Computing and Communication Technologies (RIVF\u201921). IEEE, 1\u20136."},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISI49825.2020.9280528"},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.1109\/INCET49848.2020.9153985"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aap9559"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05933-9_40"},{"key":"e_1_3_2_147_2","article-title":"\u201cLiar, liar pants on fire\u201d: A new benchmark dataset for fake news detection","author":"Wang William Yang","year":"2017","unstructured":"William Yang Wang. 2017. \u201cLiar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017).","journal-title":"arXiv preprint arXiv:1705.00648"},{"key":"e_1_3_2_148_2","article-title":"Cross-lingual cross-platform rumor verification pivoting on multimedia content","author":"Wen Weiming","year":"2018","unstructured":"Weiming Wen, Songwen Su, and Zhou Yu. 2018. Cross-lingual cross-platform rumor verification pivoting on multimedia content. arXiv preprint arXiv:1808.04911 (2018).","journal-title":"arXiv preprint arXiv:1808.04911"},{"key":"e_1_3_2_149_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615015"},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.1145\/3373464.3373475"},{"key":"e_1_3_2_151_2","first-page":"2220","volume-title":"European Conference on Artificial Intelligence (ECAI\u201920)","author":"Wu Lianwei","year":"2020","unstructured":"Lianwei Wu and Yuan Rao. 2020. Adaptive interaction fusion networks for fake news detection. In European Conference on Artificial Intelligence (ECAI\u201920). IOS Press, 2220\u20132227."},{"key":"e_1_3_2_152_2","article-title":"Next-GPT: Any-to-any multimodal LLM","author":"Wu Shengqiong","year":"2023","unstructured":"Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2023. Next-GPT: Any-to-any multimodal LLM. arXiv preprint arXiv:2309.05519 (2023).","journal-title":"arXiv preprint arXiv:2309.05519"},{"key":"e_1_3_2_153_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIBD51990.2021.9459078"},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102610"},{"key":"e_1_3_2_155_2","article-title":"Sequence-to-sequence neural net models for grapheme-to-phoneme conversion","author":"Yao Kaisheng","year":"2015","unstructured":"Kaisheng Yao and Geoffrey Zweig. 2015. Sequence-to-sequence neural net models for grapheme-to-phoneme conversion. arXiv preprint arXiv:1506.00196 (2015).","journal-title":"arXiv preprint arXiv:1506.00196"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3113981"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3114093"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-46677-9_29"},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1145\/3309699"},{"key":"e_1_3_2_160_2","article-title":"Defending against neural fake news","volume":"32","author":"Zellers Rowan","year":"2019","unstructured":"Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. Advan. Neural Inf. Process. Syst. 32 (2019).","journal-title":"Advan. Neural Inf. Process. Syst."},{"key":"e_1_3_2_161_2","article-title":"MM-LLMs: Recent advances in multimodal large language models","author":"Zhang Duzhen","year":"2024","unstructured":"Duzhen Zhang, Yahan Yu, Chenxing Li, Jiahua Dong, Dan Su, Chenhui Chu, and Dong Yu. 2024. MM-LLMs: Recent advances in multimodal large language models. arXiv preprint arXiv:2401.13601 (2024).","journal-title":"arXiv preprint arXiv:2401.13601"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206973"},{"key":"e_1_3_2_163_2","first-page":"5484","article-title":"M3Exam: A multilingual, multimodal, multilevel benchmark for examining large language models","volume":"36","author":"Zhang Wenxuan","year":"2023","unstructured":"Wenxuan Zhang, Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing. 2023. M3Exam: A multilingual, multimodal, multilevel benchmark for examining large language models. Advan. Neural Inf. Process. Syst. 36 (2023), 5484\u20135505.","journal-title":"Advan. Neural Inf. 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