{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:52:48Z","timestamp":1784829168088,"version":"3.55.0"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Online Social Networks and Media"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.osnem.2026.100345","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:38:28Z","timestamp":1773383908000},"page":"100345","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Misinformation detection on YouTube using video captions"],"prefix":"10.1016","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3892-1192","authenticated-orcid":false,"given":"Raj","family":"Jagtap","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinav","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Goel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shakshi","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3630-9811","authenticated-orcid":false,"given":"Clint","family":"Pazhayidam George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajesh","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.osnem.2026.100345_b1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.chb.2017.05.007","article-title":"The effects of news consumption via social media and news information overload on perceptions of journalistic norms and practices","volume":"75","author":"Lee","year":"2017","journal-title":"Comput. Hum. Behav."},{"issue":"1","key":"10.1016\/j.osnem.2026.100345_b2","doi-asserted-by":"crossref","first-page":"eaau4586","DOI":"10.1126\/sciadv.aau4586","article-title":"Less than you think: Prevalence and predictors of fake news dissemination on facebook","volume":"5","author":"Guess","year":"2019","journal-title":"Sci. Adv."},{"key":"10.1016\/j.osnem.2026.100345_b3","doi-asserted-by":"crossref","unstructured":"S. Sharma, R. Sharma, Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach, in: Proceedings of the International Joint Conference on Neural Network, 2021.","DOI":"10.1109\/IJCNN52387.2021.9534185"},{"issue":"1","key":"10.1016\/j.osnem.2026.100345_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07761-2","article-title":"Influence of fake news in Twitter during the 2016 US presidential election","volume":"10","author":"Bovet","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.osnem.2026.100345_b5","article-title":"Fake news as fake politics: the digital materialities of YouTube misinformation videos about Brazilian oil spill catastrophe","author":"Lemos","year":"2020","journal-title":"Media Cult. Soc."},{"key":"10.1016\/j.osnem.2026.100345_b6","first-page":"278","article-title":"Bias misperceived: The role of partisanship and misinformation in youtube comment moderation","volume":"vol. 13","author":"Jiang","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b7","series-title":"Conspiracies\u201d dominate YouTube climate modification videos","author":"Carne","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b8","article-title":"The complexity of simply searching for medical advice","author":"Diresta","year":"2018","journal-title":"Wired"},{"key":"10.1016\/j.osnem.2026.100345_b9","series-title":"COVID-RELATED misinformation ON YOUTUBE","author":"Knuutila","year":"2020"},{"issue":"CSCW1","key":"10.1016\/j.osnem.2026.100345_b10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3392854","article-title":"Measuring misinformation in video search platforms: An audit study on YouTube","volume":"4","author":"Hussein","year":"2020","journal-title":"Proc. ACM Human-Comput. Interact."},{"key":"10.1016\/j.osnem.2026.100345_b11","series-title":"Hours of video uploaded to YouTube every minute","author":"Clement","year":"2019"},{"issue":"7","key":"10.1016\/j.osnem.2026.100345_b12","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1080\/21645515.2018.1454572","article-title":"Misinformation on vaccination: A quantitative analysis of YouTube videos","volume":"14","author":"Donzelli","year":"2018","journal-title":"Hum. Vaccines Immunother."},{"issue":"5","key":"10.1016\/j.osnem.2026.100345_b13","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1513\/AnnalsATS.201809-644OC","article-title":"YouTube videos as a source of misinformation on idiopathic pulmonary fibrosis","volume":"16","author":"Goobie","year":"2019","journal-title":"Ann. Am. Thorac. Soc."},{"key":"10.1016\/j.osnem.2026.100345_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.chb.2022.107345","article-title":"What goes on inside rumour and non-rumour tweets and their reactions: A psycholinguistic analyses","author":"Butt","year":"2022","journal-title":"Comput. Hum. Behav."},{"key":"10.1016\/j.osnem.2026.100345_b15","doi-asserted-by":"crossref","unstructured":"S. Sharma, A. Datta, V. Shankaran, R. Sharma, Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination, in: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 5091\u20135095.","DOI":"10.1145\/3583780.3614746"},{"key":"10.1016\/j.osnem.2026.100345_b16","article-title":"Properties, prediction, and prevalence of useful user-generated comments for descriptive annotation of social media objects","volume":"vol. 7","author":"Momeni","year":"2013"},{"issue":"5","key":"10.1016\/j.osnem.2026.100345_b17","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1002\/asi.21291","article-title":"Text-based video content classification for online video-sharing sites","volume":"61","author":"Huang","year":"2010","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"10.1016\/j.osnem.2026.100345_b18","doi-asserted-by":"crossref","unstructured":"K. Filippova, K.B. Hall, Improved video categorization from text metadata and user comments, in: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011, pp. 835\u2013842.","DOI":"10.1145\/2009916.2010028"},{"key":"10.1016\/j.osnem.2026.100345_b19","series-title":"European Conference on Information Retrieval","first-page":"38","article-title":"Exploiting user comments for audio-visual content indexing and retrieval","author":"Eickhoff","year":"2013"},{"key":"10.1016\/j.osnem.2026.100345_b20","doi-asserted-by":"crossref","unstructured":"A.S. Do\u011fru\u00f6z, N. Ponomareva, S. Girgin, R. Jain, C. Oehler, Text based user comments as a signal for automatic language identification of online videos, in: Proceedings of the 19th ACM International Conference on Multimodal Interaction, 2017, pp. 374\u2013378.","DOI":"10.1145\/3136755.3136788"},{"key":"10.1016\/j.osnem.2026.100345_b21","article-title":"Crowds can effectively identify misinformation at scale","author":"Martel","year":"2022","journal-title":"Perspect. Psychol. Sci."},{"issue":"2","key":"10.1016\/j.osnem.2026.100345_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.im.2022.103745","article-title":"Fake news believability: The effects of political beliefs and espoused cultural values","volume":"60","author":"Gupta","year":"2023","journal-title":"Inf. Manag."},{"key":"10.1016\/j.osnem.2026.100345_b23","series-title":"Who checks the checkers? Exploring source credibility in Twitter\u2019s community notes","author":"Kangur","year":"2024"},{"issue":"CSCW1","key":"10.1016\/j.osnem.2026.100345_b24","doi-asserted-by":"crossref","DOI":"10.1145\/3392854","article-title":"Measuring misinformation in video search platforms: An audit study on YouTube","volume":"4","author":"Hussein","year":"2020","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"issue":"2","key":"10.1016\/j.osnem.2026.100345_b25","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social media and fake news in the 2016 election","volume":"31","author":"Allcott","year":"2017","journal-title":"J. Econ. Perspect."},{"key":"10.1016\/j.osnem.2026.100345_b26","article-title":"(Mis) leading the COVID-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic","author":"Sharma","year":"2022","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"10.1016\/j.osnem.2026.100345_b27","article-title":"Misinformation on instagram: The impact of trusted endorsements on message credibility","volume":"6","author":"Mena","year":"2020","journal-title":"Soc. Media+ Soc."},{"key":"10.1016\/j.osnem.2026.100345_b28","series-title":"2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining","first-page":"1092","article-title":"Analyzing disinformation and crowd manipulation tactics on YouTube","author":"Hussain","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104375","article-title":"Healthcare professionals\u2019 acts of correcting health misinformation on social media","volume":"148","author":"Bautista","year":"2021","journal-title":"Int. J. Med. Inform."},{"issue":"8","key":"10.1016\/j.osnem.2026.100345_b30","first-page":"2301","article-title":"Understanding user polarisation regarding COVID-19 vaccines through social network analysis","volume":"27","author":"Garg","year":"2024","journal-title":"J. Discret. Math. Sci. Cryptogr."},{"issue":"1","key":"10.1016\/j.osnem.2026.100345_b31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s42001-025-00443-2","article-title":"Exposing the behavior of rumour spreaders on Twitter: targeted versus non-targeted rumour","volume":"9","author":"Yang","year":"2026","journal-title":"J. Comput. Soc. Sci."},{"key":"10.1016\/j.osnem.2026.100345_b32","doi-asserted-by":"crossref","unstructured":"S. Sharma, A. Datta, R. Sharma, AMIR: An Automated Misinformation Rebuttal System\u2013A COVID-19 Vaccination Datasets-Based Exposition, in: IEEE Transactions on Computational Social Systems, 2024.","DOI":"10.1109\/TCSS.2024.3429526"},{"key":"10.1016\/j.osnem.2026.100345_b33","series-title":"Despite recent crackdown, YouTube still promotes plenty of conspiracies","author":"Weissman","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b34","series-title":"Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda","first-page":"66","article-title":"Rumor detection on social media: Datasets, methods and opportunities","author":"Li","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b35","series-title":"Proceedings of the 27th International Conference on Computational Linguistics","first-page":"3346","article-title":"Automated fact checking: Task formulations, methods and future directions","author":"Thorne","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b36","doi-asserted-by":"crossref","unstructured":"V.L. Rubin, N. Conroy, Y. Chen, S. Cornwell, Fake news or truth? using satirical cues to detect potentially misleading news, in: Proceedings of the Second Workshop on Computational Approaches To Deception Detection, 2016, pp. 7\u201317.","DOI":"10.18653\/v1\/W16-0802"},{"key":"10.1016\/j.osnem.2026.100345_b37","article-title":"Fake news: A survey of research, detection methods, and opportunities","volume":"2","author":"Zhou","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b38","series-title":"Proceedings of the 27th International Conference on Computational Linguistics","first-page":"3391","article-title":"Automatic detection of fake news","author":"P\u00e9rez-Rosas","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b39","series-title":"Proceedings of the 27th International Conference on Computational Linguistics","first-page":"1859","article-title":"A retrospective analysis of the fake news challenge stance-detection task","author":"Hanselowski","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b40","series-title":"2018 22nd Conference of Open Innovations Association","first-page":"272","article-title":"Automatic online fake news detection combining content and social signals","author":"Della Vedova","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b41","series-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","first-page":"22","article-title":"DeClarE: Debunking fake news and false claims using evidence-aware deep learning","author":"Popat","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b42","doi-asserted-by":"crossref","unstructured":"S. Volkova, K. Shaffer, J.Y. Jang, N. Hodas, Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017, pp. 647\u2013653.","DOI":"10.18653\/v1\/P17-2102"},{"key":"10.1016\/j.osnem.2026.100345_b43","unstructured":"V. Qazvinian, E. Rosengren, D. Radev, Q. Mei, Rumor has it: Identifying misinformation in microblogs, in: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 2011, pp. 1589\u20131599."},{"key":"10.1016\/j.osnem.2026.100345_b44","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","first-page":"5047","article-title":"Tree LSTMs with convolution units to predict stance and rumor veracity in social media conversations","author":"Kumar","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b45","series-title":"Proceedings of the 27th International Conference on Computational Linguistics","first-page":"3371","article-title":"Attending sentences to detect satirical fake news","author":"De Sarkar","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b46","series-title":"Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda","first-page":"92","article-title":"Neural architectures for fine-grained propaganda detection in news","author":"Gupta","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b47","series-title":"Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media","author":"Gillespie","year":"2018"},{"key":"10.1016\/j.osnem.2026.100345_b48","series-title":"Behind the Screen","author":"Roberts","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b49","series-title":"YouTube community guidelines enforcement \u2013 google transparency report","author":"Google","year":"2021"},{"issue":"5","key":"10.1016\/j.osnem.2026.100345_b50","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2020-002604","article-title":"YouTube as a source of information on COVID-19: a pandemic of misinformation?","volume":"5","author":"Li","year":"2020","journal-title":"BMJ Glob. Health"},{"key":"10.1016\/j.osnem.2026.100345_b51","series-title":"How the \u2018plandemic\u2019movie and its falsehoods spread widely online","author":"Frenkel","year":"2020"},{"key":"10.1016\/j.osnem.2026.100345_b52","doi-asserted-by":"crossref","DOI":"10.5210\/fm.v25i6.10633","article-title":"What types of covid-19 conspiracies are populated by twitter bots?","author":"Ferrara","year":"2020","journal-title":"First Monday"},{"issue":"1","key":"10.1016\/j.osnem.2026.100345_b53","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":"10.1016\/j.osnem.2026.100345_b54","first-page":"315","article-title":"Modeling and measuring expressed (dis) belief in (mis) information","volume":"vol. 14","author":"Jiang","year":"2020"},{"key":"10.1016\/j.osnem.2026.100345_b55","doi-asserted-by":"crossref","unstructured":"I. Adaji, Susceptibility of Online Users to Persuasive Strategies to Curb the Spread of Misinformation, in: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, 2023, pp. 157\u2013164.","DOI":"10.1145\/3563359.3596989"},{"key":"10.1016\/j.osnem.2026.100345_b56","series-title":"Proceedings of the International AAAI Conference on Web and Social Media","first-page":"1312","article-title":"FaCov: COVID-19 viral news and rumors fact-check articles dataset","volume":"vol. 16","author":"Sharma","year":"2022"},{"key":"10.1016\/j.osnem.2026.100345_b57","doi-asserted-by":"crossref","unstructured":"W. Iqbal, M.H. Arshad, G. Tyson, I. Castro, Exploring crowdsourced content moderation through lens of reddit during covid-19, in: Proceedings of the 17th Asian Internet Engineering Conference, 2022, pp. 26\u201335.","DOI":"10.1145\/3570748.3570753"},{"key":"10.1016\/j.osnem.2026.100345_b58","doi-asserted-by":"crossref","unstructured":"P. Juneja, M.M. Bhuiyan, T. Mitra, Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube, in: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, pp. 1\u201322.","DOI":"10.1145\/3544548.3580846"},{"issue":"CSCW2","key":"10.1016\/j.osnem.2026.100345_b59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3555611","article-title":"Who\u2019s in the crowd matters: Cognitive factors and beliefs predict misinformation assessment accuracy","volume":"6","author":"Kaufman","year":"2022","journal-title":"Proc. ACM Human-Comput. Interact."},{"key":"10.1016\/j.osnem.2026.100345_b60","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. Artificial Intelligence Res."},{"key":"10.1016\/j.osnem.2026.100345_b61","series-title":"A comparison of synthetic oversampling methods for multi-class text classification","author":"Glazkova","year":"2020"},{"key":"10.1016\/j.osnem.2026.100345_b62","series-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013"},{"key":"10.1016\/j.osnem.2026.100345_b63","doi-asserted-by":"crossref","unstructured":"J. Pennington, R. Socher, C.D. Manning, Glove: Global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2014, pp. 1532\u20131543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.1016\/j.osnem.2026.100345_b64","series-title":"2018 International Japan-Africa Conference on Electronics, Communications and Computations","first-page":"151","article-title":"A text classifier using weighted average word embedding","author":"Elsaadawy","year":"2018"},{"issue":"7","key":"10.1016\/j.osnem.2026.100345_b65","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"issue":"10","key":"10.1016\/j.osnem.2026.100345_b66","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1109\/TNNLS.2020.3019893","article-title":"Attention in natural language processing","volume":"32","author":"Galassi","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.osnem.2026.100345_b67","series-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014"},{"issue":"11","key":"10.1016\/j.osnem.2026.100345_b68","doi-asserted-by":"crossref","first-page":"5160","DOI":"10.1109\/TMTT.2021.3112199","article-title":"Self-attention Bi-LSTM networks for radar signal modulation recognition","volume":"69","author":"Wei","year":"2021","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"10.1016\/j.osnem.2026.100345_b69","series-title":"A structured self-attentive sentence embedding","author":"Lin","year":"2017"},{"key":"10.1016\/j.osnem.2026.100345_b70","series-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"10.1016\/j.osnem.2026.100345_b71","series-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024"},{"key":"10.1016\/j.osnem.2026.100345_b72","series-title":"Proceedings of the 29th International Conference on Computational Linguistics","first-page":"4741","article-title":"Unsupervised domain adaptation for text classification via meta self-paced learning","author":"Trung","year":"2022"}],"container-title":["Online Social Networks and Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468696426000017?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468696426000017?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:53:33Z","timestamp":1781034813000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2468696426000017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":72,"alternative-id":["S2468696426000017"],"URL":"https:\/\/doi.org\/10.1016\/j.osnem.2026.100345","relation":{},"ISSN":["2468-6964"],"issn-type":[{"value":"2468-6964","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Misinformation detection on YouTube using video captions","name":"articletitle","label":"Article Title"},{"value":"Online Social Networks and Media","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.osnem.2026.100345","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100345"}}