CFP last date
15 July 2024
Reseach Article

Enhancing Fake News Identification in Social Media through Ensemble Learning Methods

by Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka

Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka . Enhancing Fake News Identification in Social Media through Ensemble Learning Methods. International Journal of Applied Information Systems. 12, 41 ( Sep 2023), 1-22. DOI=10.5120/ijais2023451949

@article{ 10.5120/ijais2023451949,
author = { Timothy Moses, Henry Egaga Obi, Christopher Ifeanyi Eke, Jeffrey Agushaka },
title = { Enhancing Fake News Identification in Social Media through Ensemble Learning Methods },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2023 },
volume = { 12 },
number = { 41 },
month = { Sep },
year = { 2023 },
issn = { 2249-0868 },
pages = { 1-22 },
numpages = {9},
url = { },
doi = { 10.5120/ijais2023451949 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-14T17:32:45.348415+05:30
%A Timothy Moses
%A Henry Egaga Obi
%A Christopher Ifeanyi Eke
%A Jeffrey Agushaka
%T Enhancing Fake News Identification in Social Media through Ensemble Learning Methods
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 1-22
%D 2023
%I Foundation of Computer Science (FCS), NY, USA

The proliferation of deliberately misleading information, commonly known as fake news, poses a significant challenge in shaping public opinions. This paper presents a cutting-edge methodology for effectively identifying and combating fake news by harnessing the power of ensemble learning techniques. Recognizing the widespread influence of fake news and its detrimental societal effects, there is an urgent need for robust and adaptable identification models. Existing approaches often suffer from biases and lack adaptability due to their reliance on single algorithms or limited datasets. To address these limitations, the study introduces an ensemble learning model that incorporates a diverse range of algorithms, enhancing accuracy and adaptability across various fake news contexts. Leveraging a benchmark dataset, the established model attained an exceptional accuracy rate of 97.86% using the test dataset, outperforming existing architectures. Through this research, the researchers aim to mitigate the adverse impact of fake news on social media platforms and provide a more reliable means of content verification.

  1. M. Aldwairi and A. Alwahedi, “Detecting fake news in social media networks,” Procedia Comput. Sci., vol. 141, pp. 215–222, 2018, doi: 10.1016/j.procs.2018.10.171.
  2. Y. Liu and Y. F. B. Wu, “Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks,” in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018, pp. 354–361.
  3. T. Finneman and R. J. Thomas, “A family of falsehoods: Deception, media hoaxes and fake news,” Newsp. Res. J., vol. 39, no. 3, pp. 350–361, 2018, doi: 10.1177/0739532918796228.
  4. R. K. Kaliyar, “Fake news detection using a deep neural network,” 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1–7, 2018, doi: 10.1109/CCAA.2018.8777343.
  5. J. M. Burkhardt, “History of fake news,” Libr. Technol. Rep., vol. 53, no. 8, pp. 1–33, 2017.
  6. N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, and D. Lazer, “Political science: Fake news on Twitter during the 2016 U.S. presidential election,” Science (80-. )., vol. 363, no. 6425, pp. 374–378, 2019, doi: 10.1126/science.aau2706.
  7. D. De Beer and M. Matthee, Approaches to Identify Fake News : A Systematic Literature Review, no. Macaulay 2018. Springer International Publishing, 2021. doi: 10.1007/978-3-030-49264-9.
  8. X. Zhou and R. Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Comput. Surv., vol. 53, no. 5, 2020, doi: 10.1145/3395046.
  9. M. D. Molina, S. S. Sundar, T. Le, and D. Lee, “‘Fake News’ Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content,” Am. Behav. Sci., vol. 65, no. 2, pp. 180–212, 2021, doi: 10.1177/0002764219878224.
  10. R. Oshikawa, J. Qian, and W. Y. Wang, “A survey on natural language processing for fake news detection,” Lr. 2020 - 12th Int. Conf. Lang. Resour. Eval. Conf. Proc., pp. 6086–6093, 2020.
  11. J. Y. Khan, M. T. I. Khondaker, S. Afroz, G. Uddin, and A. Iqbal, “A benchmark study of machine learning models for online fake news detection,” Mach. Learn. with Appl., vol. 4, p. 100032, 2021, doi: 10.1016/j.mlwa.2021.100032.
  12. A. Gelfert, “Fake news: A definition,” Informal Log., vol. 38, no. 1, pp. 84–117, 2018, doi: 10.22329/il.v38i1.5068.
  13. E. C. Tandoc, Z. W. Lim, and R. Ling, “Defining ‘Fake News’: A typology of scholarly definitions,” Digit. Journal., vol. 6, no. 2, pp. 137–153, 2018, doi: 10.1080/21670811.2017.1360143.
  14. N. Mukerji, “What is Fake Make-Up?,” Fiji Times, no. 679, p. 3300, 2016, [Online]. Available:
  15. C. C. Wang, “Fake news and related concepts: Definitions and recent research development,” Contemp. Manag. Res., vol. 16, no. 3, pp. 145–174, 2020, doi: 10.7903/CMR.20677.
  16. D. M. J. Lazer et al., “The science of fake news,” Science (80-. )., vol. 359, no. 6380, pp. 1094–1096, 2018, doi: 10.1126/science.aao2998.
  17. J. P. Baptista and A. Gradim, “A Working Definition of Fake News,” Encyclopedia, vol. 2, no. 1, pp. 632–645, 2022, doi: 10.3390/encyclopedia2010043.
  18. D. Gaozhao, “Flagging fake news on social media: An experimental study of media consumers’ identification of fake news,” Gov. Inf. Q., vol. 38, no. 3, p. 101591, 2021, doi: 10.1016/j.giq.2021.101591.
  19. G. Murphy, E. F. Loftus, R. H. Grady, L. J. Levine, and C. M. Greene, “False Memories for Fake News During Ireland’s Abortion Referendum,” Psychol. Sci., vol. 30, no. 10, pp. 1449–1459, 2019, doi: 10.1177/0956797619864887.
  20. S. Banaji and R. Bhat, “WhatsApp Vigilantes: An exploration of citizen reception and circulation of WhatsApp misinformation linked to mob violence in India,” LSE Media Commun., vol. 2, pp. 1–14, 2020, [Online]. Available:
  21. S. van der Linden, C. Panagopoulos, and J. Roozenbeek, “You are fake news: political bias in perceptions of fake news,” Media, Cult. Soc., vol. 42, no. 3, pp. 460–470, 2020, doi: 10.1177/0163443720906992.
  22. M. Cantarella, N. Fraccaroli, and R. G. Volpe, “Does Fake News Affect Voting Behaviour?,” SSRN Electron. J., vol. 18, no. 6, 2020, doi: 10.2139/ssrn.3629666.
  23. T. Lee, “The global rise of ‘fake news’ and the threat to democratic elections in the USA,” Public Adm. Policy, vol. 22, no. 1, pp. 15–24, 2019, doi: 10.1108/pap-04-2019-0008.
  24. V. Bakir and A. McStay, “Fake News and The Economy of Emotions: Problems, causes, solutions,” Digit. Journal., vol. 6, no. 2, pp. 154–175, 2018, doi: 10.1080/21670811.2017.1345645.
  25. M. Keenan and K. Dillenburger, “How ‘fake news’ affects autism policy,” Societies, vol. 8, no. 2. 2018. doi: 10.3390/soc8020029.
  26. S. M. Jang and J. K. Kim, “Third person effects of fake news: Fake news regulation and media literacy interventions,” Comput. Human Behav., vol. 80, no. March, pp. 295–302, 2018, doi: 10.1016/j.chb.2017.11.034.
  27. G. E. R. Agudelo, O. J. S. Parra, and J. B. Velandia, “Raising a model for fake news detection using machine learning in Python,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11195 LNCS, pp. 596–604, 2018, doi: 10.1007/978-3-030-02131-3_52.
  28. A. Jain, A. Shakya, H. Khatter, and A. K. Gupta, “a Smart System for Fake News,” pp. 2–5, 2019.
  29. L. Waikhom and R. S. Goswami, “Fake News Detection Using Machine Learning,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3462938.
  30. K. S. Veda, K. Sudarshana, and Amulya, “A Novel Technique for Fake News Detection using Machine Learning Algorithms and Web Scrapping,” 2020, [Online]. Available:
  31. U. Sharma, S. Saran, and S. M. Patil, “Fake News Detection Using Machine Learning Algorithms,” 2020, [Online]. Available:
  32. P. A. Yerlekar, “Fake News Detection using Machine Learning Approach Multinomial Naive Bayes Classifier,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 5, pp. 1304–1308, 2021, doi: 10.22214/ijraset.2021.34509.
  33. N. Islam et al., “Ternion: An autonomous model for fake news detection,” Appl. Sci., vol. 11, no. 19, pp. 1–15, 2021, doi: 10.3390/app11199292.
  34. E. Hossain, N. Kaysar, A. Zahid, J. Uddin, M. Rahman, and W. Rahman, “(2) A Study towards Bangla Fake News Detection Using Machine Learning and Deep Learning | Request PDF,” Springer, no. December, pp. 79–95, 2022, doi: 10.1007/978-981-16-5157-1.
  35. A. M. Ali, F. A. Ghaleb, B. A. S. Al-Rimy, F. J. Alsolami, and A. I. Khan, “Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique,” Sensors, vol. 22, no. 18, 2022, doi: 10.3390/s22186970.
  36. W. A. Qader, “An Overview of Bag of Words ; Importance , Implementation , Applications , and Challenges,” 2019 Int. Eng. Conf., no. June 2019, pp. 200–204, 2020, doi: 10.1109/IEC47844.2019.8950616.
  37. Ş. B. Hayta, H. Takçi, and M. Emİnlİ, “Language identification based on n-gram feature extraction method by using classifiers N-GRAM FEATURE EXTRACTION METHOD BY USING CLASSIFIERS,” no. July 2020, 2013.
  38. T. J. Bahzad and A. M. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” no. January, 2021, doi: 10.38094/jastt20165.
  39. N. Mohapatra, K. Shreya, and A. Chinmay, “Optimization of the Random Forest Algorithm,” in International Journal of Geoinformatics, 2020, pp. 201–208. doi: 10.1007/978-981-15-0978-0_19.
  40. S. Sperandei, “Understanding logistic regression analysis,” Biochem. Medica, pp. 12–18, 2014, doi: 10.11613/BM.2014.003.
  41. T. P. Bagchi, “S upport Vector Machines,” no. January, 2022.
  42. S. Fafalios, P. Charonyktakis, and I. Tsamardinos, “Gradient Boosting Trees,” no. April, pp. 1–13, 2020, [Online]. Available:
  43. Y. Liu, Y. Zhou, S. Wen, and C. Tang, “A Strategy on Selecting Performance Metrics for Classifier Evaluation,” Int. J. Mob. Comput. Multimed. Commun., vol. 6, no. 4, pp. 20–35, 2014, doi: 10.4018/IJMCMC.2014100102.
  44. C. I. Eke, A. A. Norman, and L. Shuib, “Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model,” vol. 9, pp. 48501–48518, 2021, doi: 10.1109/ACCESS.2021.3068323.
  45. A. Tasnim, M. A. Rahman, and J. Akhter, “Performance Evaluation of Multiple Classifiers for Predicting Fake News,” pp. 1–21, 2022, doi: 10.4236/jcc.2022.109001.
  46. C. I. Eke, A. A. Norman, L. Shuib, and H. F. Nweke, “Sarcasm identification in textual data: systematic review, research challenges and open directions,” Artif. Intell. Rev., vol. 53, pp. 4215–4258, 2020.
Index Terms

Computer Science
Information Sciences
News Classification
Text Recognition


Machine Learning Fake News Ensemble Learning Identification Models Social Media