{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:34:29Z","timestamp":1777952069303,"version":"3.51.4"},"reference-count":18,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.01.004","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:30:19Z","timestamp":1774035019000},"page":"21-27","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Investigating Misleadingness in Fake News Datasets: A Computational Linguistic Approach"],"prefix":"10.1016","volume":"275","author":[{"given":"Omar A.","family":"Attia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sameh","family":"Alansary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.01.004_bib1","doi-asserted-by":"crossref","unstructured":"Adjin-Tettey, T. (2022). Combating fake news, disinformation, and misinformation: Experimental evidence for media literacy education. Cogent Arts & Humanities, 9(1), 2037229. https:\/\/doi.org\/10.1080\/23311983.2022.2037229","DOI":"10.1080\/23311983.2022.2037229"},{"key":"10.1016\/j.procs.2026.01.004_bib2","doi-asserted-by":"crossref","unstructured":"Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211\u2013236. https:\/\/doi.org\/10.1257\/jep.31.2.211","DOI":"10.1257\/jep.31.2.211"},{"key":"10.1016\/j.procs.2026.01.004_bib3","doi-asserted-by":"crossref","unstructured":"Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020). Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14), eaay3539. https:\/\/doi.org\/10.1126\/sciadv.aay3539","DOI":"10.1126\/sciadv.aay3539"},{"key":"10.1016\/j.procs.2026.01.004_bib4","unstructured":"Ananda,\u202fM. (2019). Clickbait Dataset [Data set]. Kaggle. https:\/\/www.kaggle.com\/datasets\/amananandrai\/clickbait-dataset"},{"key":"10.1016\/j.procs.2026.01.004_bib5","unstructured":"Barrault, L., Duquenne, P.-A., Elbayad, M., Kozhevnikov, A., Alastruey, B., Andrews, P., Coria, M., Couairon, G., Costa-juss\u00e0, M. R., Dale, D., Elsahar, H., Heffernan, K., Janeiro, J. M., Tran, T., Ropers, C., S\u00e1nchez, E., San Roman, R., Mourachko, A., Saleem, S., & Schwenk, H. (2024). Large concept models: Language modeling in a sentence representation space [Computer software]. GitHub. https:\/\/github.com\/facebookresearch\/large_concept_model"},{"key":"10.1016\/j.procs.2026.01.004_bib6","doi-asserted-by":"crossref","unstructured":"Biyani, P., Tsioutsiouliklis, K., & Blackmer, J. (2016). \u201c8 amazing secrets for getting more clicks\u201d: Detecting clickbaits in news streams using article informality. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16). https:\/\/doi.org\/10.1609\/aaai.v30i1.9966","DOI":"10.1609\/aaai.v30i1.9966"},{"key":"10.1016\/j.procs.2026.01.004_bib7","doi-asserted-by":"crossref","unstructured":"Bronakowski, M., Al-Khassawneh, M., & Al Bataineh, A. (2023). Automatic detection of clickbait headlines using semantic analysis and machine learning techniques. Applied Sciences, 13(4), 2456. https:\/\/doi.org\/10.3390\/app13042456","DOI":"10.3390\/app13042456"},{"key":"10.1016\/j.procs.2026.01.004_bib8","doi-asserted-by":"crossref","unstructured":"Chakraborty, A., Paranjape, B., Kakarla, S., & Ganguly, N. (2016). Stop clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of the 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016) (pp. 9\u201316). IEEE\/ACM. https:\/\/doi.org\/10.1109\/ASONAM.2016.7752207","DOI":"10.1109\/ASONAM.2016.7752207"},{"key":"10.1016\/j.procs.2026.01.004_bib9","doi-asserted-by":"crossref","unstructured":"Cooke, N. A. (2017). Posttruth, truthiness, and alternative facts: Information behavior and critical information consumption for a new age. Library Quarterly, 87(3), 211\u2013221. https:\/\/doi.org\/10.1086\/692298","DOI":"10.1086\/692298"},{"key":"10.1016\/j.procs.2026.01.004_bib10","unstructured":"De Cock Buning, M. (2018). A multi-dimensional approach to disinformation: Report of the independent high-level group on fake news and online disinformation. Luxembourg: Publications Office of the European Union."},{"key":"10.1016\/j.procs.2026.01.004_bib11","doi-asserted-by":"crossref","unstructured":"Ecker, U. K. H., Lewandowsky, S., Fenton, O., & Martin, K. (2014). Do people keep believing because they want to? Preexisting attitudes and the continued influence of misinformation. Memory & Cognition, 42(2), 292\u2013304. https:\/\/doi.org\/10.3758\/s13421-013-0358-x","DOI":"10.3758\/s13421-013-0358-x"},{"key":"10.1016\/j.procs.2026.01.004_bib12","doi-asserted-by":"crossref","unstructured":"Horne, B. D., & Adal\u0131, S. (2017). This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 759\u2013766. https:\/\/doi.org\/10.1609\/icwsm.v11i1.14976","DOI":"10.1609\/icwsm.v11i1.14976"},{"key":"10.1016\/j.procs.2026.01.004_bib13","unstructured":"ISOT Research Lab. (n.d.). ISOT Fake News Dataset [Data set]. University of Victoria. https:\/\/onlineacademiccommunity.uvic.ca\/isot\/datasets\/"},{"key":"10.1016\/j.procs.2026.01.004_bib14","unstructured":"Liu, Y., Shen, X., Zhang, Y., Wang, Z., Tian, Y., Dai, J., & Cao, Y. (2024). A systematic review of machine learning approaches for detecting deceptive activities on social media: Methods, challenges, and biases. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2410.20293"},{"key":"10.1016\/j.procs.2026.01.004_bib15","unstructured":"P\u00e9rez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2018). Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018) (pp. 3391\u20133401). Association for Computational Linguistics. https:\/\/aclanthology.org\/C18-1287\/"},{"key":"10.1016\/j.procs.2026.01.004_bib16","doi-asserted-by":"crossref","unstructured":"Petkar, P. B. (2020). Fake news detection: A survey of techniques. International Journal of Innovative Technology and Exploring Engineering, 9(9), 383\u2013386.*","DOI":"10.35940\/ijitee.I7098.079920"},{"key":"10.1016\/j.procs.2026.01.004_bib17","unstructured":"Shu,\u202fK., Mahudeswaran,\u202fD., Wang,\u202fS., Lee,\u202fD., & Liu,\u202fH. (2018). FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media [Data set]. https:\/\/github.com\/KaiDMML\/FakeNewsNet"},{"key":"10.1016\/j.procs.2026.01.004_bib18","doi-asserted-by":"crossref","unstructured":"Wu, Y., Ngai, E. W., Wu, P., & Wu, C. (2022). Fake news on the internet: A literature review, synthesis, and directions for future research. Internet Research, 32(5), 1682\u20131712. https:\/\/doi.org\/10.1108\/INTR-05-2021-0294","DOI":"10.1108\/INTR-05-2021-0294"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926000049?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926000049?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:21:07Z","timestamp":1777893667000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926000049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":18,"alternative-id":["S1877050926000049"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.01.004","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Investigating Misleadingness in Fake News Datasets: A Computational Linguistic Approach","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.01.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}