{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:12:29Z","timestamp":1772989949142,"version":"3.50.1"},"reference-count":41,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":334,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The proliferation of misinformation on social media platforms like Instagram poses a significant threat, eroding public trust and potentially endangering public health. While research in this area has grown considerably, a notable gap remains in addressing misinformation in languages other than English. This study bridges that gap by establishing a comprehensive performance benchmark for detecting misinformation on Instagram, focusing specifically on Persian\u2010language content related to COVID\u201019. To support this research, we constructed a novel labelled dataset of 27,000 Persian comments collected from Instagram. We employed five established machine learning and deep learning approaches for misinformation analysis: XGBoost (with TF\u2010IDF embedding), LSTM (with GloVe embedding), CNN, KNN and BERT. To prevent overfitting, we conducted model training and validation, evaluating performance using confusion matrices. The results demonstrate that the LSTM model achieved the best classification performance, with an accuracy of 0.97, precision of 0.91, recall of 0.85 and an F1\u2010score of 0.85. This outcome establishes the first high\u2010performance baseline for deep learning methods on this unique Persian misinformation dataset. Furthermore, the training and validation curves for the LSTM approach indicate its effectiveness in mitigating overfitting.<\/jats:p>","DOI":"10.1049\/htl2.70036","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T04:58:40Z","timestamp":1764651520000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["COVID\u201019 Persian Misinformation Detection on Instagram: A Comparative Analysis of Machine Learning and Deep Learning Methods"],"prefix":"10.1049","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9635-2363","authenticated-orcid":false,"given":"Mohammad Javad","family":"Shayegan","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Department of Computer Engineering University of Science and Culture  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elahe Moradi","family":"Pirbalouti","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Computer Engineering University of Science and Culture  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3283-1912","authenticated-orcid":false,"given":"Mo","family":"Saraee","sequence":"additional","affiliation":[{"name":"School of Science, Engineering and Environment University of Salford  Salford UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2021.11.022"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2021.106716"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.osnem.2021.100164"},{"key":"e_1_2_12_5_1","doi-asserted-by":"crossref","unstructured":"K.Papadamou S.Zannettou J.Blackburn E.De Cristofaro G.Stringhini andM.Sirivianos \u201c\u201cIt Is Just a Flu\u201d: Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations \u201d inProceedings of the International AAAI Conference on Web and Social Media (AAAI 2022) 723\u2013734.","DOI":"10.1609\/icwsm.v16i1.19329"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019600"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278\u2010020\u201000696\u2010x"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100428"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102631"},{"key":"e_1_2_12_10_1","doi-asserted-by":"crossref","unstructured":"B.Graham\u2010Kalio O.Oyebode N.Zincir\u2010Heywood andR.Orji \u201cAnalyzing COVID\u201019 Tweets Using Health Behaviour Theories and Machine Learning \u201d in2021 IEEE 9th International Conference on Serious Games and Applications for Health (SeGAH) (IEEE 2021) 1\u20138 https:\/\/doi.org\/10.1109\/SEGAH52098.2021.9551908.","DOI":"10.1109\/SEGAH52098.2021.9551908"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi13100244"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102740"},{"issue":"3","key":"e_1_2_12_13_1","first-page":"1","article-title":"Analyzing Sentiments and Diffusion Characteristics of COVID\u201019 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework","volume":"9","author":"Daradkeh M.","year":"2022","journal-title":"International Journal of Business Analytics (IJBAN)"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc6020058"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00354\u2010021\u201000151\u20101"},{"key":"e_1_2_12_16_1","volume-title":"A Deep Topical N\u2010Gram Model and Topic Discovery on COVID\u201019 News and Research Manuscripts","author":"Du Y.","year":"2021"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2021.3063257"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010022\u201007296\u20100"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010022\u201007797\u2010y"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2022.103674"},{"key":"e_1_2_12_21_1","doi-asserted-by":"crossref","unstructured":"T. S.Camelia F. R.Fahim andM. M.Anwar \u201cA Regularized LSTM Method for Detecting Fake News Articles \u201d preprint arXiv November 16 2024 https:\/\/doi.org\/10.48550\/arXiv.2411.10713.","DOI":"10.1109\/SPICSCON64195.2024.10941441"},{"key":"e_1_2_12_22_1","first-page":"1","article-title":"Hybrid Deep Learning Approach for Sentiment Analysis Using Text and Emojis","volume":"36","author":"Kuruva A.","year":"2024","journal-title":"Network: Computation in Neural Systems"},{"issue":"3","key":"e_1_2_12_23_1","first-page":"764","article-title":"A Study on Effective Feature Extraction and Genetic Algorithm Based Feature Selection Method in Fake News Detection Classification Using Machine Learning Approaches","volume":"14","author":"\u0130ncir R.","year":"2024","journal-title":"G\u00fcm\u00fcshane \u00dcniversitesi Fen Bilimleri Enstit\u00fcs\u00fc Dergisi"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979\u2010024\u201003534\u20105"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42044-025-00235-x"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227\u2010024\u201006671\u2010z"},{"key":"e_1_2_12_27_1","doi-asserted-by":"crossref","unstructured":"X.Chen W.Ma D.Li et\u00a0al. \u201cKalman\u2010Based Adaptive Moment Estimation Optimisation Algorithm to Enhance GPT in LLMs for Medical Sentiment Analysis of Patient Health\u2010Related Feedback \u201dIEEE Journal of Biomedical and Health Informatics ahead of print February 26 2025.","DOI":"10.1109\/JBHI.2025.3527340"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100247"},{"key":"e_1_2_12_29_1","doi-asserted-by":"crossref","unstructured":"A.Omar T. M.Mahmoud andT.Abd\u2010El\u2010Hafeez \u201cComparative Performance of Machine Learning and Deep Learning Algorithms for Arabic Hate Speech Detection in OSNs \u201d inThe International Conference on Artificial Intelligence and Computer Vision (Springer International Publishing 2020) 247\u2013257.","DOI":"10.1007\/978-3-030-44289-7_24"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.05.080"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010023\u201044113\u20107"},{"issue":"3","key":"e_1_2_12_32_1","first-page":"39","article-title":"Building an Effective and Accurate Associative Classifier Based on Support Vector Machine","volume":"164","author":"Farghaly H. M.","year":"2020","journal-title":"Sylwan"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537\u2010024\u201000944\u20103"},{"key":"e_1_2_12_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13008"},{"key":"e_1_2_12_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103862"},{"issue":"2","key":"e_1_2_12_36_1","first-page":"159","article-title":"Clustering Methods to Analyze Social Media Posts During Coronavirus Pandemic in Iran","volume":"10","author":"Amiri F.","year":"2022","journal-title":"Journal of AI and Data Mining"},{"issue":"3","key":"e_1_2_12_37_1","first-page":"283","article-title":"Providing a Hybrid Approach Based on Deep Learning and Machine Learning to Detect Fake News\u2010A Case Study of Persian News in the Field of COVID\u201019","volume":"8","author":"Mottaghi V.","year":"2022","journal-title":"Sciences and Techniques of Information Management"},{"issue":"4","key":"e_1_2_12_38_1","first-page":"221","article-title":"Providing a Three\u2010Dimensional Tensor Approach for Classifying and Detecting Fake News\u2010A Case Study of Persian News in the Field of COVID\u201019","volume":"7","author":"Mottaghi V.","year":"2021","journal-title":"Sciences and Techniques of Information Management"},{"key":"e_1_2_12_39_1","doi-asserted-by":"publisher","DOI":"10.24200\/sci.2024.62504.7876"},{"key":"e_1_2_12_40_1","unstructured":"E.Moradi \u201cPersian\u2010Covid\u2010Instagram \u201dGitHub (2022) published February 18 2022 https:\/\/github.com\/elahemrd\/Dataset\/blob\/main\/Persian\u2010Covid\u2010Instagram.csv."},{"key":"e_1_2_12_41_1","doi-asserted-by":"crossref","unstructured":"D.Kar \u201cSpotting Misinformation to Limit the Impact of Disruption on Society by Using Machine Learning \u201d in2020 IEEE Applied Signal Processing Conference (ASPCON) (IEEE 2020) 1\u20135 https:\/\/doi.org\/10.1109\/ASPCON49795.2020.9276723.","DOI":"10.1109\/ASPCON49795.2020.9276723"},{"issue":"4","key":"e_1_2_12_42_1","first-page":"43","article-title":"A Statistical Analysis of Persian Fake News on COVID\u201019","volume":"5","author":"Ghayoomi M.","year":"2022","journal-title":"Journal of Sociolinguistics"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70036","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/htl2.70036","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl2.70036","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T12:08:42Z","timestamp":1772971722000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl2.70036"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/htl2.70036"],"URL":"https:\/\/doi.org\/10.1049\/htl2.70036","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"value":"2053-3713","type":"print"},{"value":"2053-3713","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-03-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70036"}}