{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:16:24Z","timestamp":1784736984918,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02943-w","type":"journal-article","created":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T06:01:37Z","timestamp":1716616897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Advancements in Fake News Detection: A Comprehensive Machine Learning Approach Across Varied Datasets"],"prefix":"10.1007","volume":"5","author":[{"given":"Adeel","family":"Aslam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fazeel","family":"Abid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3761-1641","authenticated-orcid":false,"given":"Jawad","family":"Rasheed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anza","family":"Shabbir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manahil","family":"Murtaza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shtwai","family":"Alsubai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harun","family":"Elkiran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"2943_CR1","doi-asserted-by":"publisher","unstructured":"Abdulrahman A, Baykara M. Fake news detection using machine learning and deep learning algorithms. In: 2020 international conference on advanced science and engineering (ICOASE). 2020. https:\/\/doi.org\/10.1109\/icoase51841.2020.9436605.","DOI":"10.1109\/icoase51841.2020.9436605"},{"key":"2943_CR2","doi-asserted-by":"crossref","unstructured":"Ahmed H, Traore I, Saad S. Detection of online fake news using N-gram analysis and machine learning techniques. 2017","DOI":"10.1007\/978-3-319-69155-8_9"},{"key":"2943_CR3","doi-asserted-by":"crossref","unstructured":"Ahmed H, Traore I, Saad S. Detecting opinion spams and fake news using text classification. J Secur Priv. 2018;1(1) (Wiley).","DOI":"10.1002\/spy2.9"},{"issue":"4","key":"2943_CR4","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1080\/15377857.2014.959691","volume":"15","author":"MA Amazeen","year":"2016","unstructured":"Amazeen MA. Checking the fact-checkers in 2008: predicting political ad scrutiny and assessing consistency. J Political Mark. 2016;15(4):433\u201364.","journal-title":"J Political Mark"},{"key":"2943_CR5","unstructured":"Arora P, Pandey M. Fake news detection using machine learning: a survey. 2020."},{"key":"2943_CR6","doi-asserted-by":"publisher","unstructured":"Baptista JP, Gradim A. Understanding fake news consumption: a reviaew. MDPI. 2020. https:\/\/doi.org\/10.3390\/socsci9100185","DOI":"10.3390\/socsci9100185"},{"key":"2943_CR7","doi-asserted-by":"publisher","first-page":"37","DOI":"10.24989\/ocg.v.342.2","volume":"342","author":"I Beutel","year":"2022","unstructured":"Beutel I, Kirschler O, Kokott S. How do fake news and hate speech affect political discussion and target persons and how can they be detected? Central and Eastern European eDem and eGov Days. 2022;342:37\u201381.","journal-title":"Central and Eastern European eDem and eGov Days"},{"key":"2943_CR8","unstructured":"Bharadwaj P, Shao Z. Detecting fake news: a deep learning approach. arXiv preprint arXiv:1903.05781. 2019"},{"key":"2943_CR9","unstructured":"Bozku E. Fake news detection datasets. Kaggle. 2022. https:\/\/www.kaggle.com\/datasets\/emineyetm\/fake-news-detection-datasets"},{"key":"2943_CR10","doi-asserted-by":"crossref","unstructured":"Capuano N, Fenza G, Loia V, Nota FD. Content based fake news detection with machine and deep learning: a systematic review.\u00a0Neurocomputing. 2023.","DOI":"10.1016\/j.neucom.2023.02.005"},{"key":"2943_CR11","unstructured":"Chelmis C, Cambria E, SI L. Fake news detection on social media: a data mining perspective. ACM Trans Data Sci. 2019."},{"key":"2943_CR12","doi-asserted-by":"publisher","unstructured":"Chen L, Zhang T, Li T. Gradient boosting model for unbalanced quantitative mass spectra quality assessment. In: 2017 International conference on security, pattern analysis, and cybernetics (SPAC). 2017. https:\/\/doi.org\/10.1109\/spac.2017.8304311","DOI":"10.1109\/spac.2017.8304311"},{"key":"2943_CR13","unstructured":"Chen Y, Zhang J, Wu L, Yin D, Zhou X. Fake news detection with human-in-the-loop. 2021."},{"key":"2943_CR14","unstructured":"Wu C, Dongol B, Sifa R, Winslett M. Fake news detection on social media using a multimodal approach. In: Proceedings of the 2020 ACM multimedia conference. 2020."},{"key":"2943_CR15","unstructured":"Da San Martino G, Strapparava C. Leveraging linguistic patterns for fake news detection and mitigation. In: Proceedings of the 27th International conference on computational linguistics. 2018."},{"key":"2943_CR16","unstructured":"Ghenai A, Davey ARH. Real-time classification of news into fact-checked categories. In: IEEE\/ACM International conference on advances in social networks analysis and mining. 2018."},{"key":"2943_CR17","doi-asserted-by":"crossref","unstructured":"Granik M, Mesyura V. Fake news detection using naive Bayes classifier. In\u00a02017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON). IEEE. 2017. pp. 900\u2013903.","DOI":"10.1109\/UKRCON.2017.8100379"},{"key":"2943_CR18","unstructured":"Granik M, Mayer KJ, Sukthankar G. Fake news detection on social media: a data mining perspective. In: ACM transactions on data science. 2018."},{"key":"2943_CR19","unstructured":"Islam F, Barbier G, Rokne J. Fake news detection in social media using linguistic metaproperties. ACM Trans Inf Syst. 2018."},{"key":"2943_CR20","unstructured":"Jiang B, Shi Y, Song Y, Li C, Zhang W. Fake news detection with attention mechanism. 2020."},{"key":"2943_CR21","doi-asserted-by":"crossref","unstructured":"Kabalci Y, Ko\u00e7aba\u015f B. Fake news detection using machine learning: an ensemble learning approach. J Ambient Intell Humaniz Comput. 2020.","DOI":"10.1155\/2020\/8885861"},{"key":"2943_CR22","unstructured":"Kaliyar RK. A comprehensive survey on fake news detection techniques. In Handbook of research on applications and implementations of machine learning and artificial intelligence. 2021."},{"key":"2943_CR23","doi-asserted-by":"publisher","unstructured":"Kaliyar RK, Goswami A, Narang P. Multiclass fake news detection using ensemble machine learning. In: 2019 IEEE 9th international conference on advanced computing (IACC). 2019. https:\/\/doi.org\/10.1109\/iacc48062.2019.8971579","DOI":"10.1109\/iacc48062.2019.8971579"},{"issue":"8","key":"2943_CR24","doi-asserted-by":"publisher","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","volume":"80","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar RK, Goswami A, Narang P. Fakebert: fake news detection in social media with a Bert-based deep learning approach. Multimed Tools Appl. 2021;80(8):11765\u201388. https:\/\/doi.org\/10.1007\/s11042-020-10183-2.","journal-title":"Multimed Tools Appl"},{"key":"2943_CR25","unstructured":"Karimi H, Tang J. Fake news detection: a hierarchical discourse-level structure (HDSF). 2021."},{"key":"2943_CR26","unstructured":"Kaur S, Kaur P. A multilevel fake news detection system using ensemble learning. Int J Mach Learn Cybern. 2019."},{"key":"2943_CR27","unstructured":"Kaur S, Kaur P. Fake news detection using hybrid technique of machine learning classifiers. In Soft Computing and Signal Processing. 2021."},{"key":"2943_CR28","doi-asserted-by":"publisher","unstructured":"Kudarvalli H, Fiaidhi J. Detecting fake news using machine learning algorithms. 2020. https:\/\/doi.org\/10.36227\/techrxiv.12089133","DOI":"10.36227\/techrxiv.12089133"},{"key":"2943_CR29","unstructured":"Li Z, Zhang Y, Wang S (2021) Fake news detection with transfer learning."},{"key":"2943_CR30","unstructured":"Liu J, Cao Y, Lin C, Shu K, Wang S. Fake news detection with multimodal analysis. 2020."},{"key":"2943_CR31","unstructured":"Liu Y, Wu YFB. Early detection of fake news on social media through propagation path classification with recurrent and Convolutional Networks. New Jersey Institute of Technology. https:\/\/researchwith.njit.edu\/en\/publications\/early-detection-of-fake-news-on-social-media-through-propagation-. 1970"},{"key":"2943_CR32","unstructured":"Mandical RR, Thampi GT, George G, Mathew G (2019) Fake news classification using machine learning: a comprehensive evaluation. Fut Gen Comput Syst."},{"key":"2943_CR33","unstructured":"Mishra R, Bhatia K, Singh A. Fake news detection with recurrent neural networks. 2019. arXiv preprint arXiv:1903.08989."},{"key":"2943_CR34","doi-asserted-by":"crossref","unstructured":"Olan F, Jayawickrama U, Arakpogun EO, Suklan J, Liu S. Fake news on social media: the Impact on Society. Inf Syst Front. 2022; 1\u201316.","DOI":"10.1007\/s10796-022-10242-z"},{"key":"2943_CR35","doi-asserted-by":"crossref","unstructured":"Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., Stein, B., & Holzinger, A. (2017). A Stylometric Inquiry into Hyperpartisan and Fake News.","DOI":"10.18653\/v1\/P18-1022"},{"key":"2943_CR36","doi-asserted-by":"crossref","unstructured":"Preotiuc-Pietro D, Gaman M, Aletras N. Automatically identifying complaints in social media. 2019. arXiv.org. https:\/\/arxiv.org\/abs\/1906.03890","DOI":"10.18653\/v1\/P19-1495"},{"key":"2943_CR37","unstructured":"Puri H, Apte V. Detecting fake news on social media using geolocation information. 2020."},{"key":"2943_CR38","doi-asserted-by":"publisher","unstructured":"Qian F, Gong C, Sharma K, Liu Y. Neural user response generator: Fake news detection with collective user intelligence. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence. 2018. https:\/\/doi.org\/10.24963\/ijcai.2018\/533","DOI":"10.24963\/ijcai.2018\/533"},{"issue":"7","key":"2943_CR39","doi-asserted-by":"publisher","first-page":"1398","DOI":"10.3390\/sym14071398","volume":"14","author":"J Rasheed","year":"2022","unstructured":"Rasheed J. Analyzing the effect of filtering and feature-extraction techniques in a machine learning model for identification of infectious disease using radiography imaging. Symmetry. 2022;14(7):1398. https:\/\/doi.org\/10.3390\/sym14071398.","journal-title":"Symmetry"},{"key":"2943_CR40","doi-asserted-by":"crossref","unstructured":"Shu K, Mahudeswaran D, Wang S, Lee D, Liu H. Fake news detection on social media: a data mining perspective. ACM Comput. 2019.","DOI":"10.1007\/978-3-031-01915-9"},{"issue":"1","key":"2943_CR41","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media. ACM SIGKDD Explor Newsl. 2017;19(1):22\u201336. https:\/\/doi.org\/10.1145\/3137597.3137600.","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"2943_CR42","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. 2017. arXiv.org. https:\/\/arxiv.org\/abs\/1708.01967"},{"key":"2943_CR43","unstructured":"Singh V, Khamparia A, Gudnavar S. Fake news detection using support vector machine and linguistic inquiry word count. In: Advanced data analytics for improved business outcomes. 2020."},{"key":"2943_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3339994","author":"T Tahir","year":"2023","unstructured":"Tahir T, Gence S, Rasool G, Umer T, Rasheed J, Yeo SF, Cevik T. Early software defects density prediction: training the international software benchmarking cross projects data using supervised learning. IEEE Access. 2023. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339994.","journal-title":"IEEE Access"},{"key":"2943_CR45","unstructured":"Verma A, Agrawal P, Hussain M, Varma V. Fake news detection with adversarial learning. 2020"},{"key":"2943_CR46","unstructured":"Wang X, Yu F, Jiang L, Meng D. Fake news detection with meta-learning. 2021."},{"key":"2943_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e15108","author":"S Waziry","year":"2023","unstructured":"Waziry S, Wardak AB, Rasheed J, Shubair RM, Rajab K, Shaikh A. Performance comparison of machine learning driven approaches for classification of complex noises in quick response code images. Heliyon. 2023. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e15108.","journal-title":"Heliyon."},{"key":"2943_CR48","unstructured":"Wu H, Yang P, Zhang C, Wu W, Yu Y (2020) Fake news detection with fact-checking"},{"key":"2943_CR49","unstructured":"Zhang S, Dou D, Liu N, Tang J, Lin CY, Cheng X. Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST). 2019."},{"key":"2943_CR50","unstructured":"Zhang Y, Huang W, Liu Y (2021) Fake news detection with crowdsourcing."},{"key":"2943_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31863-9_5","author":"H Zhao","year":"2016","unstructured":"Zhao H, Chen X, Nguyen T, Huang JZ, Williams G, Chen H. Stratified over-sampling bagging method for random forests on imbalanced data. Intell Secur Inform. 2016. https:\/\/doi.org\/10.1007\/978-3-319-31863-9_5.","journal-title":"Intell Secur Inform"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02943-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02943-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02943-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T06:06:14Z","timestamp":1716617174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02943-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["2943"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02943-w","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,25]]},"assertion":[{"value":"24 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"583"}}