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Fake News Detection Through ML and Deep Learning Approaches for Better Accuracy

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Advances in Computational Intelligence and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 399))

Abstract

Social media form a very frequent podium to people to freely express their opinions and easily communicate to others. Nowadays, it plays a vital role for spreading the news headlines, and it became most applicable news sources globally as easily accessible, but also risky as exposure of “fake news” misleads the people. The extensive spread of such misinformation deploys negative impacts on people and society and becomes recently a global problem. Several issues already rise in worlds during elections process, due to huge spread of fake news. Therefore, the detection of it on social platform transforms into an emerging research that is exciting enormous concern. Problem to identifying the fake news has concentration to public as well as government organization. Such propaganda probably affects the opinion of people and malicious parties involved to manipulate the conclusion. Due to the majority of society opinion impact changes, fake news detection is an important challenge to researchers. The detection of misinformation is not an easy task for anyone, but quite is a complex for people. Here, we analyze the different fake news detection approaches followed in current scenario and compute the detection process through machine learning and deep leaning algorithms for better accuracy.

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References

  1. Domenico GD, Visentin M (2020) Fake news or true lies? Reflections about problematic contents in marketing. Int J Mark Res 62(4):409–417. https://doi.org/10.1177/1470785320934719

    Article  Google Scholar 

  2. Fake news—political scandal words. [Online]. Available https://www.merriam-webster.com/words-at-play/politicalscandal-words/fake-news

  3. Ajao O, Bhowmik D, Zargari S (2019) Sentiment aware fake news detection on online social networks. In: ICASSP 2019. 978-1-5386-4658-8/18/$31.00 ©2019 IEEE, pp 2507–2511

    Google Scholar 

  4. Kaur S, Kumar P, Kumaraguru P (2019) Automating fake news detection system usingmulti-level voting model. SpringerVerlag GmbH Germany, part of Springer Nature 2019. https://doi.org/10.1007/s00500-019-04436-y

  5. Elhadad MK, Li KF, Gebali F (2019) Fake news detection on social media: a systematic survey. 978-1-7281-2794-1/19/$31.00 ©2019 IEEE

    Google Scholar 

  6. Xu K, Wang F, Wang H, Yang B (2020) Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Sci Technol 25(1):20–27. ISSN 1007-0214 03/14. https://doi.org/10.26599/TST.2018.9010139

  7. Rajesh K, Kumar A, Kadu R (2019) Fraudulent news detection using machine learning approaches. In: 2019 global conference for advancement in technology, India. 978-1-7281-3694.3/19/$31.00 ©2019 IEEE

    Google Scholar 

  8. Vereshchaka A, Cosimini S, Dong W (2020) Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. In: Computational and mathematical organization theory. S.I.: SBP-BRIMS 2019, © Springer Science+Business Media, LLC, part of Springer Nature 2020.https://doi.org/10.1007/s10588-020-09307-8

  9. Reddy H, Raj N, Gala M, Basava A (2020) Text-mining-based Fake News Detection Using Ensemble Methods. IJAC, © Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2020. https://doi.org/10.1007/s11633-019-1216-5

  10. de Oliveira NR, Medeiros DSV, Mattos DMF (2020) A sensitive stylistic approach to identify fake news on social networking. IEEE Sig Process Lett. https://doi.org/10.1109/LSP.2020.3008087

  11. Rubin VL, Chen Y, Conroy NJ (2015) Deception detection for news: three types of fakes. In: 78th ASIS&T annual meeting: information science with impact: research in and for the community. American Society for Information Science, p 83

    Google Scholar 

  12. Shi P, Zhang Z, Kwang K, Choo R (2019) Detecting malicious social bots based on clickstream sequences. IEEE Access.https://doi.org/10.1109/ACCESS.2019.2901864

  13. Ghafari SM, Beheshti A, Joshi A, Paris C, Mahmood A, Yakhchi S, Orgun MA (2020) A survey on trust prediction in online social networks. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3009445

    Article  Google Scholar 

  14. Shrivastava G, Kumar P, Ojha RP, Srivastava PK, Mohan S, Srivastava G (2020) Defensive modeling of fake news through online social networks. IEEE Trans Comput Soc Sys. © IEEE 2020. https://doi.org/10.1109/TCSS.2020.3014135

  15. Umer M, Imtiaz Z, Ullah S, Mehmood A, Choi GS, On BW (2016) Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access. https://doi.org/10.1109/ACCESS.2017

    Article  Google Scholar 

  16. Han W, Mehta V (2019) Fake news detection in social networks using machine learning and deep learning: performance evaluation. In: 2019 IEEE ICII. 978-1-7281-2977-8/19/$31.00 ©2019 IEEE. https://doi.org/10.1109/ICII.2019.00070

  17. Hanz K, Kingsland ES (2020) Fake or for real? A fake news workshop. Ref Serv Rev 48(1):91–112. © Emerald Publishing Limited, 0090-7324. https://doi.org/10.1108/RSR-09-2019-0064

  18. Reis JCS, Correia A, Murai F, Veloso A, Benevenuto F (2019) Supervised learning for fake news detection. Affective computing and sentiment analysis. IEEE Intell Syst. 1541-1672_2019 IEEE, Published by the IEEE Computer Society. https://doi.org/10.1109/MIS.2019.2899143

  19. Sahoo SR, Gupta BB (2021) Multiple features based approach for automatic fake news detection on social networks using deep learning. Appl Soft Comput 100:106983

    Google Scholar 

  20. Choudhary M, Chouhan SS, Pilli ES, Vipparthi SK (2021) BerConvoNet: a deep learning framework for fake news classification. Appl Soft Comput 110:107614

    Google Scholar 

  21. Jindal R, Dahiya D, Sinha D, Garg A (2022) A study of machine learning techniques for fake news detection and suggestion of an ensemble model. In: International conference on innovative computing and communications. Springer, Singapore, pp 627–637

    Google Scholar 

  22. Sharma DK, Garg S (2021) IFND: a benchmark dataset for fake news detection. Complex Intell Syst 1–21

    Google Scholar 

  23. Zervopoulos A, Alvanou AG, Bezas K, Papamichail A, Maragoudakis M, Kermanidis K (2021) Deep learning for fake news detection on Twitter regarding the 2019 Hong Kong protests. Neural Comput Appl 1–14

    Google Scholar 

  24. Kaliyar RK, Goswami A, Narang P (2021) FakeBERT: fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools Appl 80(8):11765–11788

    Article  Google Scholar 

  25. Khanam Z, Alwasel BN, Sirafi H, Rashid M (2021, March) Fake news detection using machine learning approaches. IOP Conf Ser Mater Sci Eng 1099(1):012040. IOP Publishing

    Google Scholar 

  26. Divya TV, Banik BG (2021) A walk through various paradigms for fake news detection on social media. In: Proceedings of international conference on computational intelligence and data engineering. Springer, Singapore, pp 173–183

    Google Scholar 

  27. Dubey AK, Singhal A, Gupta S (2020) Rumor detection system using machine learning. Int Res J Eng Technol (IRJET) 07(05). e-ISSN 2395-0056

    Google Scholar 

  28. Hakak S, Alazab M, Khan S, Gadekallu TR, Maddikunta PKR, Khan WZ (2021) An ensemble machine learning approach through effective feature extraction to classify fake news. Futur Gener Comput Syst 117:47–58

    Article  Google Scholar 

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Authors and Affiliations

  1. ABES Engineering College Ghaziabad, Ghaziabad, Uttar Pradesh, 201009, India

    Anil Kumar Dubey & Mala Saraswat

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  1. Anil Kumar Dubey
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  2. Mala Saraswat
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Editor information

Editors and Affiliations

  1. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  2. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. National Institute of Technology Agartala, Agartala, Tripura, India

    Munesh C. Trivedi

  4. Krishna Institute of Engineering and Technology, Ghaziabad, Uttar Pradesh, India

    Pradeep Kumar Singh

  5. Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dubey, A.K., Saraswat, M. (2022). Fake News Detection Through ML and Deep Learning Approaches for Better Accuracy. In: Gao, XZ., Tiwari, S., Trivedi, M.C., Singh, P.K., Mishra, K.K. (eds) Advances in Computational Intelligence and Communication Technology. Lecture Notes in Networks and Systems, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-16-9756-2_2

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  • DOI: https://doi.org/10.1007/978-981-16-9756-2_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9755-5

  • Online ISBN: 978-981-16-9756-2

  • eBook Packages: EngineeringEngineering (R0)

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