Skip to content
2000
Volume 19, Issue 9
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047
file format text download EPUB
file format pdf download PDF
side by side viewer icon HTML

Abstract

Aims and Introduction

As the COVID-19 pandemic develops, there is a lot of false information floating around on social media, and the risks are huge. This is why identifying and countering disinformation campaigns is so important. Modern deep learning models that employ Natural-Language-Processing (NLP) approaches, such as Bidirectional-Encoder-Representations-from-Transformers (BERT), have been quite effective at identifying disinformation.

Objectives and Methods

To tackle the spread of false information on COVID-19, we present an explainable NLP approach that utilizes DistilBERT and Shapley-Additive-exPlanations (SHAP), two powerful and efficient frameworks. A dataset consisting of 984 assertions regarding COVID-19 that were factually verified was initially compiled. The DistilBERT model achieved superior performance in spotting COVID-19 disinformation after we doubled the dataset's sample size using back-translation.

Results and Discussion

Compared to more conventional machine learning models, it performed better on both datasets. Additionally, we used SHAP to enhance the explainability of the models, which were then tested in amid-subjects experimentation with three constraints: text(T), text+SHAP explanation(TSE), and text+SHAP explanation+source and evidence(TSESE). The goal was to increase public trust in the models' predictions.

Conclusion

Compared to the T condition, the TSE &TSESE constraints showed a substantial increase in participants' confidence and sharing of COVID-19-related information. Improving public trust and identifying COVID-19 disinformation were two important outcomes of our study.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121313149240729044309
2024-09-04
2025-11-29

  • From This Site

    /content/journals/eng/10.2174/0118722121313149240729044309
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/eng/19/9/ENG-19-9-19.html?itemId=/content/journals/eng/10.2174/0118722121313149240729044309&mimeType=html&fmt=ahah

References

  1. QayyumA. AhmadK. AhsanM.A. Al-FuqahaA. QadirJ. Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge.IEEE Open J. Comput. Soc.2022317218410.1109/OJCS.2022.3206407
     [Google Scholar]
  2. YooJ.H. JeongH. LeeJ. ChungT.M. Open problems in medical federated learning.Int. J. Web Inform. Syst.2022182/3779910.1108/IJWIS‑04‑2022‑0080
     [Google Scholar]
  3. DurgaR. PoovammalE. Fled-block: Federated learning ensembled deep learning blockchain model for COVID-19 prediction.Front. Public Health20221089249910.3389/fpubh.2022.892499 35784262
     [Google Scholar]
  4. VarmaR. VermaY. VijayvargiyaP. ChuriP.P. A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic.Int. J. Intell. Comput. Cybernet.202114461764610.1108/IJICC‑04‑2021‑0069
     [Google Scholar]
  5. SarkarS. AgrawalS. GadekalluT.R. MahmudM. BrownD.J. Privacy-preserving federated learning for pneumonia diagnosis. Neural Information Processing.ChamSpringer2022345356
     [Google Scholar]
  6. SucharithaY. ReddyP.C.S. ChittiT.N. Deep learning based framework for crop yield predictionAIP Conf. Proc.202325481810.1063/5.0118526
     [Google Scholar]
  7. AhsanM.M. AlamT.E. HaqueM.A. AliM.S. RifatR.H. NafiA.A.N. HossainM.M. IslamM.K. Enhancing Monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning.Inform. Med. Unlock.202445101449
     [Google Scholar]
  8. KumarG.R. ReddyR.V. JayarathnaM. PughazendiN. VidyullathaS. ReddyP.C.S. Web application based Diabetes prediction using Machine Learning2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)202317Erode, India10.1109/ACCAI58221.2023.10200323
     [Google Scholar]
  9. AlghamdiJ. LinY. LuoS. A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection.Information (Basel)2022131257610.3390/info13120576
     [Google Scholar]
  10. GuoZ. YuK. JolfaeiA. BashirA.K. AlmagrabiA.O. KumarN. Fuzzy detection system for rumors through explainable adaptive learning.IEEE Trans. Fuzzy Syst.202129123650366410.1109/TFUZZ.2021.3052109
     [Google Scholar]
  11. SaraswatD. BhattacharyaP. VermaA. PrasadV.K. TanwarS. SharmaG. BokoroP.N. SharmaR. Explainable AI for healthcare 5.0: Opportunities and challenges.IEEE Access202210844868451710.1109/ACCESS.2022.3197671
     [Google Scholar]
  12. PatilS.B. RaoH.R. ChatrapathyK. KiranA. KumarA.S. ReddyP.C.S. Ensemble deep learning framework for classification of skin lesions2023 International Conference on Circuit Power and Computing Technologies (ICCPCT)202315501555Kollam, India10.1109/ICCPCT58313.2023.10244850
     [Google Scholar]
  13. JinB. XuX. Forecasting wholesale prices of yellow corn through the Gaussian process regression.Neural Comput. Appl.202436158693871010.1007/s00521‑024‑09531‑2
     [Google Scholar]
  14. JinB. XuX. Price forecasting through neural networks for crude oil, heating oil, and natural gas. Measurement.Energy2024100001
     [Google Scholar]
  15. KumariR. AshokN. GhosalT. EkbalA. A multitask learning approach for fake news detection: Novelty, emotion, and sentiment lend a helping hand2021 International Joint Conference on Neural Networks (IJCNN)202118Shenzhen, China10.1109/IJCNN52387.2021.9534218
     [Google Scholar]
  16. LathaS.B. DastagiraiahC. KiranA. AsifS. ElangovanD. ReddyP.C.S. An adaptive machine learning model for Walmart sales prediction2023 International Conference on Circuit Power and Computing Technologies (ICCPCT)2023988992Kollam, India10.1109/ICCPCT58313.2023.10245029
     [Google Scholar]
  17. KhanW.Z. AzamF. KhanM.K. Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions.IEEE Trans. Artif. Intell.202242210228
     [Google Scholar]
  18. PrasadV.K. BhattacharyaP. MaruD. TanwarS. VermaA. SinghA. TiwariA.K. SharmaR. AlkhayyatA. ȚurcanuF.E. RaboacaM.S. Federated learning for the internet-of-medical-things: A survey.Mathematics202211115110.3390/math11010151
     [Google Scholar]
  19. MuazuT. MaoY. MuhammadA.U. IbrahimM. KumsheU.M.M. SamuelO. A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing.Comput. Commun.202421616818210.1016/j.comcom.2024.01.006
     [Google Scholar]
  20. ShahidW. JamshidiB. HakakS. IsahH. KhanW.Z. KhanM.K. ChooK.K.R. Detecting and mitigating the dissemination of fake news: Challenges and future research opportunities.IEEE Transactions on Computational Social Systems (Early Access)202211446494662
     [Google Scholar]
  21. RamanaA.V. BhogaU. DhulipallaR.K. KiranA. CharyB.D. ReddyP.C.S. Abnormal Behavior Prediction in Elderly Persons Using Deep Learning2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3)202310.1109/IC2E357697.2023.10262547
     [Google Scholar]
  22. SudhakarB. SikrantP.A. PrasadM.L. LathaS.B. KumarG.R. SarikaS. Shaker ReddyP.C. Brain tumor image prediction from MR images using CNN based deep learning networks.J. Inf. Technol. Manage.20241614460
     [Google Scholar]
  23. AliH. JavedR.T. QayyumA. AlGhadhbanA. AlazmiM. AlzamilA. AlUtaibiK. QadirJ. SPAM-DaS: Secure and privacy-aware misinformation detection as a serviceAuthorea Preprints2023
     [Google Scholar]
  24. LiH. LiC. WangJ. YangA. MaZ. ZhangZ. HuaD. Review on security of federated learning and its application in healthcare.Future Gener. Comput. Syst.202314427129010.1016/j.future.2023.02.021
     [Google Scholar]
  25. PrasadM.L. KiranA. Shaker ReddyP.C. Chronic kidney disease risk prediction using machine learning techniques.J. Inf. Technol. Manage.2024161118134
     [Google Scholar]
  26. MadhaviG.B. BhavaniA.D. ReddyY.S. KiranA. ChitraN.T. ReddyP.C.S. Traffic congestion detection from surveillance videos using deep learning. Traffic Congestion Detection from Surveillance Videos using Deep Learning.2023 Srinagar Garhwal, India10.1109/IC2E357697.2023.10262545
     [Google Scholar]
  27. EndoP.T. SantosG.L. de Lima XavierM.E. Nascimento CamposG.R. de LimaL.C. SilvaI. EgliA. LynnT. Illusion of Truth: Analysing and classifying COVID-19 fake news in Brazilian Portuguese language.Big Data Cogn Comput2022623610.3390/bdcc6020036
     [Google Scholar]
  28. SuneelS. BalaramA. Amina BegumM. UmapathyK. ReddyP.C.S. TalasilaV. Quantum mesh neural network model in precise image diagnosing.Opt. Quantum Electron.202456455910.1007/s11082‑023‑06245‑y
     [Google Scholar]
  29. PuraivanE. VenegasR. RiquelmeF. An empiric validation of linguistic features in machine learning models for fake news detection.Data Knowl. Eng.202314710220710.1016/j.datak.2023.102207
     [Google Scholar]
  30. RekhaM.N. PrasadM.L. MukherjeeS. NikamS.V. SharmaS. ReddyP.C.S. An automatic error recognition approach for machine translation results based on deep learning 2024 2nd International Conference on Computer, Communication and Control (IC4)2024 Indore, India10.1109/IC457434.2024.10486776
     [Google Scholar]
  31. ShanmugavelS.B. Devi RangaswamyK. MuthukannanM. A Deep Learning Model to Detect Fake News about COVID-19.Recent Adv. Comp. Sci. Commun.2023169e25082322033110.2174/2666255816666230825100307
     [Google Scholar]
  32. HakakS. KhanW.Z. BhattacharyaS. ReddyG.T. ChooK.K.R. Propagation of fake news on social media: Challenges and opportunities.Computational Data and Social Networks9 2020345353
     [Google Scholar]
  33. KozikR. FiccoM. PawlickaA. PawlickiM. PalmieriF. ChoraśM. When explainability turns into a threat - using xAI to fool a fake news detection method.Comput. Secur.202413710359910.1016/j.cose.2023.103599
     [Google Scholar]
  34. DeivasigamaniS. RaniA.J.M. NatchadalingamR. VijayakarthikP. KumarG.B.S. ReddyP.C.S. Crop yield prediction using deep reinforcement learning2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON)202313714210.1109/TEECCON59234.2023.10335861
     [Google Scholar]
  35. AyoubJ. YangX.J. ZhouF. Combat COVID-19 infodemic using explainable natural language processing models.Inf. Process. Manage.202158410256910.1016/j.ipm.2021.102569 33776192
     [Google Scholar]
  36. JayakodyN. MohammadA. HalgamugeM.N. Fake news detection using a decentralized deep learning model and federated learning IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society2022 Brussels, Belgium10.1109/IECON49645.2022.9968358
     [Google Scholar]
  37. GadupudiA. RaniR.Y. JayaramB. SharmaN. DeshmukhJ.K. ReddyP.C.S. An adaptive deep learning model for crop yield prediction2024 2nd International Conference on Computer, Communication and Control (IC4)20241510.1109/IC457434.2024.10486733
     [Google Scholar]
  38. BaswarajD. NatchadalingamR. An accurate stock prediction using ensemble deep learning model2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)202317
     [Google Scholar]
  39. DadgarS. GhateeM. Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectivesarXiv:2107.097682021
     [Google Scholar]
  40. XuX. ZhangY. Corn cash price forecasting with neural networks.Comput. Electron. Agric.202118410612010.1016/j.compag.2021.106120
     [Google Scholar]
  41. XuX. ZhangY. Price forecasts of ten steel products using Gaussian process regressions.Eng. Appl. Artif. Intell.202312610687010.1016/j.engappai.2023.106870
     [Google Scholar]
  42. WarmanD. KabirM.A. COVID Fake Explainer: An explainable machine learning based web application for detecting COVID-19 fake newsarXiv:2310.13890202310.1109/CSDE59766.2023.10487649
     [Google Scholar]
  43. JeyasudhaJ. SethP. UshaG. TannaP. Fake information analysis and detection on pandemic in twitter.SN Comput. Sci.20223645610.1007/s42979‑022‑01363‑y 36035506
     [Google Scholar]
  44. KhandayA.M.U.D. BhushanB. JhaveriR.H. KhanQ.R. RautR. RabaniS.T. Nnpcov19: Artificial neural network-based propaganda identification on social media in COVID-19 era.Mob. Inf. Syst.2022202211010.1155/2022/3412992
     [Google Scholar]
  45. AleneziM.N. AlqenaeiZ.M. Machine learning in detecting COVID-19 misinformation on twitter.Future Internet2021131024410.3390/fi13100244
     [Google Scholar]
  46. De MagistrisG. RussoS. RomaP. StarczewskiJ.T. NapoliC. An explainable fake news detector based on named entity recognition and stance classification applied to COVID-19.Information (Basel)202213313710.3390/info13030137
     [Google Scholar]
  47. YadalaS. PundruC.S.R. SolankiV.K. A novel private encryption model in iot under cloud computing domain.The International Conference on Intelligent Systems & NetworksSpringer Nature: Singapore202326327010.1007/978‑981‑99‑4725‑6_33
     [Google Scholar]
  48. RaoK.R. PrasadM.L. KumarG.R. NatchadalingamR. HussainM.M. ReddyP.C.S. Time-series cryptocurrency forecasting using ensemble deep learning2023 International Conference on Circuit Power and Computing Technologies (ICCPCT)202314461451Kollam, India10.1109/ICCPCT58313.2023.10245083
     [Google Scholar]
  49. MamathaB. RashmiD. TiwariK.S. SikrantP.A. JovithA.A. ReddyP.C.S. Lung cancer prediction from CT images and using deep learning techniques2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON)202326326710.1109/TEECCON59234.2023.10335801
     [Google Scholar]
  50. XuX. ZhangY. House price forecasting with neural networks.Intelligent Systems with Applications20211220005210.1016/j.iswa.2021.200052
     [Google Scholar]
  51. RaoK.R. KumariM.S. EklarkerR. ReddyP.C.S. MuleyK. BurugariV.K. An adaptive deep learning framework for prediction of agricultural yield2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)20241610.1109/ICICACS60521.2024.10498465
     [Google Scholar]
/content/journals/eng/10.2174/0118722121313149240729044309
Loading
A Hybrid Framework for Fake News Detection Using Explainability of Artificial Intelligence
Recent Pat Eng 19, E18722121313149 (2025); https://doi.org/10.2174/0118722121313149240729044309
/content/journals/eng/10.2174/0118722121313149240729044309
/content/journals/eng/10.2174/0118722121313149240729044309
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): artificial intelligence; COVID-19; machine learning; Misinformation detection; natural-language-processing; twitter

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test