International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

Unmasking Deception: A Comprehensive Survey on Fake News Detection Strategies and Technologies


Affiliations
1 Department of Computer Science, American International University-Bangladesh, Bangladesh
 

Fake news threatens public debate and decision-making in a digital age. This comprehensive paper, "Unmasking Deception," methodically covers false news detecting tactics and technology. We summarize a wide range of study results, methods, and technological advances to give a thorough overview of disinformation detection and mitigation. Our research covers linguistic, content-based, machine learning, and deep learning false news identification. We examine emerging misleading strategies and propose novel remedies using natural language processing, network analysis, and other innovative methods. In addition, we evaluate current detection systems in real-world circumstances and address the ethical implications of their use. The findings of the research help scholars, policymakers, and technology developers understand false news and advance the area. The primary objective is to enhance the safeguarding of the information environment against misinformation by a critical evaluation of existing methodologies.

Keywords

false, detection, digital, debate, decision-making, ecosystem.
User
Notifications
Font Size

  • Singh B, Sharma DK (2021) Predicting image credibility in fake news over social media using multi-modal approach, Neural Compute Apply

  • Yang C, Zhou X, Zafarani R (2021) CHECKED: Chinese COVID-19 fake news dataset, Soc Netw Anal Mining, 11(58) Ying L, Yu H, Wang J, Ji Y, Qian S (2021a) multi-level multi-modal.

  • Kim G, Ko Y (2021) Effective fake news detection using graph and summarization techniques. Pattern Recogn Lett 151:135–139

  • Vereshchaka A, Cosimini S, Dong W (2020) Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. Comput Math Organ Theory 26:350–364

  • Ribeiro Bezerra JF (2021) Content-based fake news classification through modified voting ensemble. J Inf Telecommun. https:// doi. org/ 10. 1080/ 24751 839. 2021. 19639 12

  • Sharma DK, Garg S (2021) IFND: a benchmark dataset for fake news detection, Compl Intell System.

  • Mridha MF, Keya AJ, Hamid MA, Monowar MM, Rahman MS (2021) A comprehensive review on fake news detection with deep learning. IEEE Access 9:156151–156170

  • Meneses Silva CV, Silva Fontes R, Colaço Júnior M (2021) Intelligent fake news detection: a systematic mapping, J Appl Secur Res, 16(2)

  • Simko J, Racsko P, Tomlein M, Hanakova M, Moro R, Bielikova M (2021) A study of fake news reading and annotating in social media context, New Rev Hypermedia Multimed

  • Silva A, Han Y, Luo L, Karunasekera S, Leckie C (2021) Propagation2Vec: embedding partial propagation networks for explainable fake news early detection. Inf Process Manag. https:// doi. org/ 10. 1016/j. ipm. 2021. 102618

  • Chauhan T, Palivela H (2021) Optimization and improvement of fake news detection using deep learning approaches for societal benefit. Int J Inf Manag Data Insights. https:// doi. org/ 10. 1016/j. jjimei. 2021. 100051

  • Supanya Aphiwongsophon et al. “Detecting Fake News with Machine Learning Method.” 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology (ECTI-CON). Chiang Rai, Thailand, Thailand: IEEE . 2018.

  • Islam, N.; Shaikh, A.; Qaiser, A.; Asiri, Y.; Almakdi, S.; Sulaiman, A.; Moazzam, V.; Babar, S.A. Ternion: An Autonomous Model for Fake News Detection. Appl. Sci. 2021, 11, 9292. https://doi.org/ 10.3390/app11199292

  • Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2017. A stylometric inquiry into hyperpartisan and fake news. CoRR abs/1702.05638. http://arxiv.org/abs/1702.05638

  • Stahl, K. (2018). Fake News Detection in social media. California State University Stanislaus, Department of Mathematics and Department of Computer Sciences. 1 University Circle, Turlock, CA 95382. Received 20 April 2018; accepted 15 May 2018.

  • Aphiwongsophon, Supanya, and Prabhas Chongstitvatana. "Detecting fake news with machine learning methods." 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications & Information Technology (ECTI-CON). IEEE, 2018.

  • Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2017). Automatic Detection of Fake News. Retrieved from http://arxiv.org/abs/1708.07104v1

  • Jain, Anjali, et al. "A smart system for fake news detection using machine learning." 2019 International conference on issues and challenges in intelligent computing techniques (ICICT). Vol. 1. IEEE, 2019

  • Helmstetter, S., & Paulheim, H. (2018, August). Weakly supervised learning for fake news detection on Twitter. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 274-277). IEEE

  • Niall J Conroy, Victoria L Rubin, and Yimin Chen. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1):1– 4, 2015.

  • M. Bugueño, G. Sepulveda, and M. Mendoza, ‘‘An empirical analysis of rumor detection on microblogs with recurrent neural networks,’’ in Proc. Int. Conf. Hum.-Comput. Interact., Jul. 2019, pp. 293–310. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3- 030-21902- 4_21

  • Baarir, N. F., & Djeffal, A. (2020). Fake News Detection Using Machine Learning. In 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), Computer Science Department, Mohamed Khider University of Biskra.

  • Shu, K., Wang, S., & Liu, H. (2019). Beyond News Contents: The Role of Social Context for Fake News Detection. In WSDM '19, February 11–15, 2019, Melbourne, VIC, Australia.

  • Akshay Jain and AmeyKasbe. “Fake News Detection.” 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Bhopal, India: IEEE. 2018.

  • Yumeng Qin et al. “Predicting Future Rumours.” Chinese Journal of Electronics (Volume: 27 , Issue: 3 , 5 2018, 514 – 520.

  • Akshay Jain and AmeyKasbe. “Fake News Detection.” 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Bhopal, India: IEEE. 2018

  • Khanam, Z., Ahsan, M.N.”Evaluating the effectiveness of test driven development: advantages and pitfalls.”International. J. Appl. Eng. Res. 12, 7705–7716, 2017

  • . Khanam, Z. “Analyzing refactoring trends and practices in the software industry.” Int. J. Adv. Res. Comput. Sci. 10, 0976–5697, 2018.

  • Veronica Perez-Rosas et al. Available at: https://www.researchgate.net/publication/3192559 85_Automatic_Detection_of_Fake_News August, 2017.

  • Prabhjot Kaur et al. “Hybrid Text Classification Method for Fake News Detection.” International Journal of Engineering and Advanced Technology (IJEAT), 2388-2392. 2019. [30]. Looijenga, M. S. “The Detection of Fake Messages using Machine Learning.” 29 Twente Student Conference on IT, Jun. 6th, 2018, Enschede, The Netherlands. Netherlands: essay.utwente.nl. 2018.

  • I. Traore et al. “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques.” International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments (pp. 127– 138). Springer International Publishing. 2017.

  • Khanam Z., Alkhaldi S. “An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System.” In: Smys S., Bestak R., Rocha Á. (eds) Inventive Computation Technologies. ICICIT 2019. Lecture Notes in Networks and Systems, vol 98. Springer, Cham. 2019.

  • Khanam Z. and Ahsan M.N. “Implementation of the pHash algorithm for face recognition in secured remote online examination system.” International Journal of Advances in Scientific Research and Engineering (ijasre) Volume 4, Issue 11 November. 2018.

  • Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 1-42.

  • Bhatt, G.; Sharma, A.; Sharma, S.; Nagpal, A.; Raman, B.; Mittal, A. Combining neural, statistical and external features for fake news stance identification. In Proceedings of the WWW ’18: Companion Proceedings of the The Web Conference 2018, Geneva, Switzerland, 23–27 April 2018; pp. 1353–1357.

  • .Bourgonje, P.; Schneider, J.M.; Rehm, G. From clickbait to fake news detection: An approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP workshop: Natural Language Processing Meets Journalism, Copenhagen, Denmark, 2 May 2017; pp. 84–89.

  • Ghanem, B.; Rosso, P.; Rangel, F. Stance detection in fake news a combined feature representation. In Proceedings of the First Workshop on Fact Extraction and Verification (FEVER), Brussels, Belgium, 1 November 2018; pp. 66–71.

  • Ciampaglia, G., Shiralkar, P., Rocha, L., Bollen, J. Menczer, F., & Flammini, A. (2015). Computational fact checking from knowledge networks.

  • Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, and Filippo Menczer. 2015. Factchecking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15 Companion, pages 977–982, New York, NY, USA. ACM.

  • Victoria Rubin, Niall Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pages 7–17, San Diego, California, June. Association for Computational Linguistics.

  • Prabhjot Kaur et al. “Hybrid Text Classification Method for Fake News Detection.” International Journal of Engineering and Advanced Technology (IJEAT), 2388-2392. 2019. [30]. Looijenga, M. S. “The Detection of Fake Messages using Machine Learning.” 29 Twente Student Conference on IT, Jun. 6th, 2018, Enschede, The Netherlands. Netherlands: essay.utwente.nl. 2018.

  • I. Traore et al. “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques.” International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments (pp. 127– 138). Springer International Publishing. 2017.

  • Khanam Z., Alkhaldi S. “An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System.” In: Smys S., Bestak R., Rocha Á. (eds) Inventive Computation Technologies. ICICIT 2019. Lecture Notes in Networks and Systems, vol 98. Springer, Cham. 2019.

  • Khanam Z. and Ahsan M.N. “Implementation of the pHash algorithm for face recognition in secured remote online examination system.” International Journal of Advances in Scientific Research and Engineering (ijasre) Volume 4, Issue 11 November. 2018.

  • Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST), 10(3), 1-42.

  • Bhatt, G.; Sharma, A.; Sharma, S.; Nagpal, A.; Raman, B.; Mittal, A. Combining neural, statistical and external features for fake news stance identification. In Proceedings of the WWW ’18: Companion Proceedings of the The Web Conference 2018, Geneva, Switzerland, 23–27 April 2018; pp. 1353–1357.

  • .Bourgonje, P.; Schneider, J.M.; Rehm, G. From clickbait to fake news detection: An approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP workshop: Natural Language Processing Meets Journalism, Copenhagen, Denmark, 2 May 2017; pp. 84–89.

  • Ghanem, B.; Rosso, P.; Rangel, F. Stance detection in fake news a combined feature representation. In Proceedings of the First Workshop on Fact Extraction and Verification (FEVER), Brussels, Belgium, 1 November 2018; pp. 66–71.

  • Ciampaglia, G., Shiralkar, P., Rocha, L., Bollen, J. Menczer, F., & Flammini, A. (2015). Computational fact checking from knowledge networks.

  • Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, and Filippo Menczer. 2015. Factchecking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15 Companion, pages 977–982, New York, NY, USA. ACM.

  • Victoria Rubin, Niall Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pages 7–17, San Diego, California, June. Association for Computational Linguistics.

  • Ksieniewicz P, Zyblewski P, Choras´ M, Kozik R, Giełiczyk A, Wo´zniak M (2020) Fake news detection from data streams. In 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8

  • Das, Bijoyan, Chakraborty, Sarit (2018) An Improved Text Sentiment Classification Model Using TF-IDFand Next Word Negation. arxiv. arXiv:1806.06407

  • Fu X, Liu W, Xu Y, Cui L (2017) Combine hownet lexicon to train phrase recursive autoencoder forsentence-level sentiment analysis. Neurocomputing 241:18–27

  • Cerisara C, Kral P, Lenc L (2018) On the effects of using word2vec representations in neural networksfor dialogue act recognition. Comput Speech Language 47:175–193

  • Bezdan T, Stoean C, Naamany AA, Bacanin N, Rashid TA, Zivkovic M, Venkatachalam K (2021) Hybrid fruit-fy optimization algorithm with kmeans for text document clustering. Mathematics 9(16):1929

  • A. Adadi and M. Berrada, ‘‘Peeking inside the black-box: A survey on explainable artificial intelligence (XAI),’’ IEEE Access, vol. 6, pp. 52138–52160, 2018.

  • X. Zhou and R. Zafarani, ‘‘Fake news detection: Interdisciplinary research,’’ in Proc. Companion World Wide Web Conf., May 2019, p. 1292.

  • A. Thota, P. Tilak, S. Ahluwalia, and N. Lohia, ‘‘Fake news detection: A deep learning approach,’’ SMU Data Sci. Rev., vol. 1, no. 3, p. 10, 2018.

  • Khanam Z, Alwasel BN, Siraf H, Rashid M (2021) Fake news detection using machine learning approaches. In: IOP conference series: Materials science and engineering. IOP Publishin, 1099(1), 1012–1040

  • Hiramath CK, Deshpande GC (2019) Fake news detection using deep learning techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT), IEEE, 411–415

  • Zhou Z, Guan H, Bhat MM, Hsu J (2019) Fake news detection via NLP is vulnerable to adversarial attacks. arXiv preprint arXiv:1901.09657

  • Aggarwal A, Mittal M, Pathak A, Goyal LM (2020) Fake news detection using a blend of neural networks: An application of deep learning. SN Comput Sci 1(3):1–9

  • Khan JY, Khondaker M, Islam T, Iqbal A, Afroz S (2019) A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749, 1–12.

  • Aphiwongsophon, Supanya, and Prabhas C Shu K, Liu H (2019) Detecting fake news on social media. Synth Lect Data Min Knowl Discov 11(3):1–129 .

  • Ahmad I, Yousaf M, Yousaf S, Ahmad MO (2020) Fake news detection using machine learning ensemble methods. Complexity 1–11

  • Long Y (2017) Fake news detection through multiperspective speaker profles. Assoc Comput Linguist 252–256

  • Kong SH, Tan LM, Gan KH, Samsudin NH (2020) Fake news detection using deep learning. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 102–107

  • Xu W, Wu J, Liu Q, Wu S, Wang L (2022) Mining fne-grained semantics via graph neural networks for evidence-based fake news detection. In: Proceedings of the ACM Web Conference 2022, 1-15

  • Kaliyar RK (2018) Fake news detection using a deep neural network. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), (1–7)

  • Shu K, Mahudeswaran D, Liu H (2019) FakeNewsTracker: a tool for fake news collection, detection, and visualization. Comput Math Organ Theory 25(1):60–71

  • Nithya S Hannah, Sahayadhas Arun (2023) Metaheuristic Searched-Ensemble Learning for fake news detection with optimal weighted feature selection approach. Data Knowl Eng 144:102124

  • Narang Poonam, Singh Ajay Vikram, Monga Himanshu (2022) Hybrid metaheuristic approach for detection of fake news on social media. Int J Performability Eng 18.6.

  • J. Ding, Y. Hu, and H. Chang, ‘‘BERT-based mental model, a better fake news detector,’’ in Proc. 6th Int. Conf. Comput. Artif. Intell., New York, NY, USA, Apr. 2020, pp. 396–400, doi: 10.1145/3404555.3404607

  • A. Giachanou, G. Zhang, and P. Rosso, ‘‘Multimodal multi-image fake news detection,’’ in Proc. IEEE 7th Int. Conf. Data Sci. Adv. Anal. (DSAA), Oct. 2020, pp. 647–654.

  • D. Mangal and D. K. Sharma, ‘‘Fake news detection with integration of embedded text cues and image features,’’ in Proc. 8th Int. Conf. Rel., INFOCOM Technol. Optim., Trends Future Directions (ICRITO), Jun. 2020, pp. 68–72.

  • P. Qi, J. Cao, T. Yang, J. Guo, and J. Li, ‘‘Exploiting multi-domain visual information for fake news detection,’’ in Proc. IEEE Int. Conf. Data Mining (ICDM), Nov. 2019, pp. 518–527.

  • R. Kumari and A. Ekbal, ‘‘AMFB: Attention based multimodal factorized bilinear pooling for multimodal fake news detection,’’ Expert Syst. Appl., vol. 184, Dec. 2021, Art. no. 115412.

  • A. Nascita, A. Montieri, G. Aceto, D. Ciuonzo, V. Persico, and A. Pescape, ‘‘XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures,’’ IEEE Trans. Netw. Service Manage., early access, Jul. 19, 2021, doi:

  • S. S. Jadhav and S. D. Thepade, ‘‘Fake news identification and classification using DSSM and improved recurrent neural network classifier,’’ Appl. Artif. Intell., vol. 33, no. 12, pp. 1058–1068, Oct. 2019, doi: 10.1080/08839514.2019.1661579.

  • S. Deepak and B. Chitturi, ‘‘Deep neural approach to Fake-News identification,’’ Proc. Comput. Sci., vol. 167, pp. 2236–2243, Jan. 2020. [Online]. Available: http://www.sciencedirect. com/science/article/pii/S1877050920307420

  • M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. S. Choi, and B.-W. On, ‘‘Fake news stance detection using deep learning architecture (CNNLSTM),’’ IEEE Access, vol. 8, pp. 156695–156706, 2020.

  • Niall J Conroy, Victoria L Rubin, and Yimin Chen. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1):1– 4, 2015.

  • Baarir, N. F., & Djeffal, A. (2020). Fake News Detection Using Machine Learning. In 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), Computer Science Department, Mohamed Khider University of Biskra.

  • Nasir JA, Khan OS, Varlamis I (2021) Fake news detection: A hybrid CNN-RNN based deep learning approach. Int J Inf Manag Data Insights 1(1):100007.

  • Saikia P, Gundale K, Jain A, Jadeja D, Patel H, Roy M (2022) Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach. arXiv. https://doi.org/10.48550/arXiv.2207.13500

  • Shu, K., Wang, S., & Liu, H. (2019). Beyond News Contents: The Role of Social Context for Fake News Detection. In WSDM '19, February 11–15, 2019, Melbourne, VIC, Australia.

  • Khanam Z, Alwasel BN, Siraf H, Rashid M (2021) Fake news detection using machine learning approaches. In: IOP conference series: Materials science and engineering. IOP Publishin, 1099(1), 1012–1040

  • Agarwal A, Mittal M, Pathak A, Goyal LM (2020) Fake news detection using a blend of neural networks: An application of deep learning. SN Comput Sci 1(3):1–9

  • Khan JY, Khondaker M, Islam T, Iqbal A, Afroz S (2019) A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749, 1–12.

  • Es PAGE (1954) CONTINUOUS INSPECTION SCHEMES. Biometrika 41(1–2):100–115. https://doi. org/10.1093/biomet/41.1-2.100

  • Zhou Z, Guan H, Bhat MM, Hsu J (2019) Fake news detection via NLP is vulnerable to adversarial attacks. arXiv preprint arXiv:1901.09657

  • Shu K, Liu H (2019) Detecting fake news on social media. Synth Lect Data Min Knowl Discov 11(3):1–129 .

  • Ahmad I, Yousaf M, Yousaf S, Ahmad MO (2020) Fake news detection using machine learning ensemble methods. Complexity 1–11

  • A. Bani-Hani, O. Adedugbe, E. Benkhelifa, M. Majdalawieh and F. Al-Obeidat, "A Semantic Model forContext-Based Fake News Detection on Social Media," in 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), Antalya, Turkey, 2020 pp. 1-7. https://doi.org/10. 1109/AICCSA50499.2020.9316504

  • Zhou Z, Guan H, Bhat MM, Hsu J (2019) Fake news detection via NLP is vulnerable to adversarial attacks. arXiv preprint arXiv:1901.09657

  • Hiramath CK, Deshpande GC (2019) Fake news detection using deep learning techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT), IEEE, 411–415

  • Kong SH, Tan LM, Gan KH, Samsudin NH (2020) Fake news detection using deep learning. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 102–107

  • Xu W, Wu J, Liu Q, Wu S, Wang L (2022) Mining fne-grained semantics via graph neural networks for evidence-based fake news detection. In: Proceedings of the ACM Web Conference 2022, 1-15

  • Silva M, R., R. Pires P, Almeida TA (2023) Incremental Learning for Fake News Detection. J Inform DataManage 13(6).

  • Long Y (2017) Fake news detection through multiperspective speaker profles. Assoc Comput Linguist 252–256

  • Gayathri, A., Radhika, S., & Jayalakshmi, S. L. (2019). Detecting fake accounts in media application using machine learning. International Journal of Advanced Networking and Applications, 234-237.

  • Lalitha, R., Praveen, P., Prasanna, K., & Monash, K. (2019). A Survey on Detection of False Data Injection Attacks in Smart Grid Communication Systems. International Journal of Advanced Networking and Applications, 106-110.


Abstract Views: 158

PDF Views: 1




  • Unmasking Deception: A Comprehensive Survey on Fake News Detection Strategies and Technologies

Abstract Views: 158  |  PDF Views: 1

Authors

Md. Faruk Abdullah AL Sohan
Department of Computer Science, American International University-Bangladesh, Bangladesh
Nusrat Jahan Trisna
Department of Computer Science, American International University-Bangladesh, Bangladesh
Rahul Das Joy
Department of Computer Science, American International University-Bangladesh, Bangladesh

Abstract


Fake news threatens public debate and decision-making in a digital age. This comprehensive paper, "Unmasking Deception," methodically covers false news detecting tactics and technology. We summarize a wide range of study results, methods, and technological advances to give a thorough overview of disinformation detection and mitigation. Our research covers linguistic, content-based, machine learning, and deep learning false news identification. We examine emerging misleading strategies and propose novel remedies using natural language processing, network analysis, and other innovative methods. In addition, we evaluate current detection systems in real-world circumstances and address the ethical implications of their use. The findings of the research help scholars, policymakers, and technology developers understand false news and advance the area. The primary objective is to enhance the safeguarding of the information environment against misinformation by a critical evaluation of existing methodologies.

Keywords


false, detection, digital, debate, decision-making, ecosystem.

References