ICCK Transactions on Intelligent Systematics Home About Editors Current Issue
  1. Home
  2. Journals
  3. ICCK Transactions on Intelligent Systematics
  4. Volume 1, Issue 3
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
Research Article | Published: 12 December 2024
ICCK Transactions on Intelligent Systematics Volume 1, Issue 3, 2024: 203-214
Volume 1, Issue 3, ICCK Transactions on Intelligent Systematics
Volume 1, Issue 3, 2024
Submit Manuscript Edit a Special Issue
Academic Editor
Jianlei Kong
Jianlei Kong
Beijing Technology and Business University, China
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Case Studies on Integrating Artificial Intelligence in Finance to Transform Decision Making and Risk Management for Enhanced Financial Outcomes Reinforcement Learning for Prompt Optimization in Language Models: A Comprehensive Survey of Methods, Representations, and Evaluation Challenges AI and the Future of Education: Advancing Personalized Learning and Intelligent Tutoring Systems Reservoir Science: A Multi-Coupling Communication Platform to Promote Energy Transformation, Climate Change and Environmental Protection From CO$_2$ Sequestration to Hydrogen Storage: Further Utilization of Depleted Gas Reservoirs Effects of Crosslinking Agents and Reservoir Conditions on the Propagation of Fractures in Coal Reservoirs During Hydraulic Fracturing Plant Disease Detection Using Deep Learning Techniques Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application The Influence of Geological Factors and Transmission Fluids on the Exploitation of Reservoir Geothermal Resources: Factor Discussion and Mechanism Analysis Current Status and Development Prospects of Carbon Capture, Utilization, and Storage (CCUS) in China: Technical, Policy, and Market Perspectives
ICCK Transactions on Intelligent Systematics, Volume 1, Issue 3, 2024: 203-214

Free to Read | Research Article | 12 December 2024
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
by
Muhammad Nadeem Muhammad Nadeem 1,3
Parchamdar Abbas Parchamdar Abbas 2
Wei Zhang Wei Zhang 1,3 *
Sumaira Rafique Sumaira Rafique 4 *
Sundas Iqbal Sundas Iqbal 5
1 College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
2 Department of Computer Science, National University of Modern Languages (NUML), Islamabad, Pakistan
3 Faculty of Information technology and Engineering, Ocean University of China, Qingdao 266100, China
4 National University of Computer and Emerging Sciences, Lahore Campus, Lahore 54700, Pakistan
5 School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
* Corresponding Authors: Wei Zhang, [email protected] ; Sumaira Rafique, [email protected]
DOI: 10.62762/TIS.2024.461943
ARK: ark:/57805/tis.2024.461943
Received: 13 November 2024, Accepted: 05 December 2024, Published: 12 December 2024  
Cited by: 3  (Source: Web of Science), 2  (Source: Scopus ), 8  (Source: Google Scholar)
   PDF  (1.58 MB)
Article Metrics Cite This Article
Abstract
The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.
Graphical Abstract
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
Keywords
fake news
natural language processing
statistical technique
machine learning
maximum likelihood estimation
social media
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. De Oliveira, N. R., Pisa, P. S., Lopez, M. A., de Medeiros, D. S. V., & Mattos, D. M. (2021). Identifying fake news on social networks based on natural language processing: trends and challenges. Information, 12(1), 38.
    [CrossRef]   [Google Scholar]
  2. Posetti, J., & Matthews, A. (2018). A short guide to the history of ‘fake news’ and disinformation. International Center for Journalists, 7(2018), 2018-07.
    [Google Scholar]
  3. Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018, April). Fake news detection in social networks via crowd signals. In Companion proceedings of the the web conference 2018 (pp. 517-524).
    [CrossRef]   [Google Scholar]
  4. Su, Q., Wan, M., Liu, X., & Huang, C. R. (2020). Motivations, methods and metrics of misinformation detection: an NLP perspective. Natural Language Processing Research, 1(1), 1-13.
    [CrossRef]   [Google Scholar]
  5. Ribeiro, F. N., Saha, K., Babaei, M., Henrique, L., Messias, J., Benevenuto, F., ... & Redmiles, E. M. (2019, January). On microtargeting socially divisive ads: A case study of russia-linked ad campaigns on facebook. In Proceedings of the conference on fairness, accountability, and transparency (pp. 140-149).
    [CrossRef]   [Google Scholar]
  6. Usman, M. T., Khan, H., Singh, S. K., Lee, M. Y., & Koo, J. (2024). Efficient deepfake detection via layer-frozen assisted dual attention network for consumer imaging devices. IEEE Transactions on Consumer Electronics.
    [CrossRef]   [Google Scholar]
  7. Tardáguila, C., Benevenuto, F., & Ortellado, P. (2018). Fake News Is Poisoning Brazilian Politics. WhatsApp Can Stop It. International New York Times, NA-NA.
    [Google Scholar]
  8. Collins, B., Hoang, D. T., Nguyen, N. T., & Hwang, D. (2021). Trends in combating fake news on social media–a survey. Journal of Information and Telecommunication, 5(2), 247-266.
    [CrossRef]   [Google Scholar]
  9. Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 53(5), 1-40.
    [CrossRef]   [Google Scholar]
  10. Wang, W. Y. (2017). `` liar, liar pants on fire'': A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648.
    [Google Scholar]
  11. Rubin, V. L. (2010). On deception and deception detection: Content analysis of computer‐mediated stated beliefs. Proceedings of the American Society for Information Science and Technology, 47(1), 1-10.
    [CrossRef]   [Google Scholar]
  12. Rubin, V. L., Conroy, N., Chen, Y., & Cornwell, S. (2016, June). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection (pp. 7-17).
    [Google Scholar]
  13. Gottfried, J., & Shearer, E. (2016). News use across social media platforms 2016. https://apo.org.au/node/64483
    [Google Scholar]
  14. Campan, A., Cuzzocrea, A., & Truta, T. M. (2017, December). Fighting fake news spread in online social networks: Actual trends and future research directions. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4453-4457). IEEE.
    [CrossRef]   [Google Scholar]
  15. Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096.
    [CrossRef]   [Google Scholar]
  16. Chadwick, A., & Vaccari, C. (2019). News sharing on UK social media: Misinformation, disinformation, and correction. Retrieved from https://repository.lboro.ac.uk/articles/News_sharing_on_UK_social_media_misinformation_disinformation_and_correction/9471269/files/17095679.pdf
    [Google Scholar]
  17. Kogan, S., Moskowitz, T. J., & Niessner, M. (2019). Fake news: Evidence from financial markets. Available at SSRN, 3237763.
    [Google Scholar]
  18. Zafarani, R., Zhou, X., Shu, K., & Liu, H. (2019, July). Fake news research: Theories, detection strategies, and open problems. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3207-3208).
    [CrossRef]   [Google Scholar]
  19. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.
    [Google Scholar]
  20. Thompson, S. A. (2017, December 14). President Trump’s lies, the definitive list. The New York Times - Breaking News, US News, World News and Videos. Retrieved from https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html
    [Google Scholar]
  21. Aïmeur, E., Amri, S., & Brassard, G. (2023). Fake news, disinformation and misinformation in social media: a review. Social Network Analysis and Mining, 13(1), 30.
    [CrossRef]   [Google Scholar]
  22. Graauwmans, V. V. (2016). Fake News in the Online World: An Experimental Study on Credibility Evaluations of Fake News depending on Information Processing Bachelor Thesis Tilburg University. http://arno.uvt.nl/show.cgi?fid=143285
    [Google Scholar]
  23. Guderlei, M., & Aßenmacher, M. (2020, December). Evaluating unsupervised representation learning for detecting stances of fake news. In Proceedings of the 28th international conference on computational linguistics (pp. 6339-6349).
    [CrossRef]   [Google Scholar]
  24. Al-Makhadmeh, Z., & Tolba, A. (2020). Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach. Computing, 102(2), 501-522.
    [CrossRef]   [Google Scholar]
  25. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
    [CrossRef]   [Google Scholar]
  26. Anderson, J. (1983). Lix and rix: Variations on a little-known readability index. Journal of Reading, 26(6), 490-496.
    [Google Scholar]
  27. Amorim, E., Cançado, M., & Veloso, A. (2018, June). Automated essay scoring in the presence of biased ratings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 229-237).
    [CrossRef]   [Google Scholar]
  28. An, J., & Weber, I. (2016). \# greysanatomy vs.\# yankees: Demographics and Hashtag Use on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 10, No. 1, pp. 523-526).
    [CrossRef]   [Google Scholar]
  29. Ahmed, H., Traore, I., & Saad, S. (2017). Detection of online fake news using n-gram analysis and machine learning techniques. In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments: First International Conference, ISDDC 2017, Vancouver, BC, Canada, October 26-28, 2017, Proceedings 1 (pp. 127-138). Springer International Publishing.
    [Google Scholar]
  30. An, J., & Kwak, H. (2017, May). What gets media attention and how media attention evolves over time: large-scale empirical evidence from 196 countries. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 11, No. 1, pp. 464-467).
    [CrossRef]   [Google Scholar]
  31. Srivastava, A. (2020). Real time fake news detection using machine learning and NLP. Int. Res. J. Eng. Technol.(IRJET), 7(06).
    [Google Scholar]
  32. Madani, M., Motameni, H., & Mohamadi, H. (2022). Fake news detection using deep learning integrating feature extraction, natural language processing, and statistical descriptors. Security and Privacy, 5(6), e264.
    [CrossRef]   [Google Scholar]
  33. Lakshmanarao, A., Swathi, Y., & Kiran, T. S. R. (2019). An effecient fake news detection system using machine learning. International Journal of Innovative Technology and Exploring Engineering, 8(10), 3125-3129.
    [Google Scholar]
  34. Hiramath, C. K., & Deshpande, G. C. (2019, July). Fake news detection using deep learning techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 411-415). IEEE.
    [CrossRef]   [Google Scholar]
  35. Mahir, E. M., Akhter, S., & Huq, M. R. (2019, June). Detecting fake news using machine learning and deep learning algorithms. In 2019 7th international conference on smart computing & communications (ICSCC) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  36. Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) (pp. 900-903). IEEE.
    [CrossRef]   [Google Scholar]
  37. Aldwairi, M., & Alwahedi, A. (2018). Detecting fake news in social media networks. Procedia Computer Science, 141, 215-222.
    [CrossRef]   [Google Scholar]
  38. Gadekar, P. S. (2019). Fake News Identification using Machine Learning. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 7(V).
    [Google Scholar]
  39. Ivancová, K., Sarnovský, M., & Maslej-Krcšñáková, V. (2021, January). Fake news detection in Slovak language using deep learning techniques. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000255-000260). IEEE.
    [CrossRef]   [Google Scholar]
  40. Meesad, P. (2021). Thai fake news detection based on information retrieval, natural language processing and machine learning. SN Computer Science, 2(6), 425.
    [Google Scholar]
  41. Cai, Y., Pan, S., Wang, X., Chen, H., Cai, X., & Zuo, M. (2020). Measuring distance-based semantic similarity using meronymy and hyponymy relations. Neural Computing and Applications, 32, 3521-3534.
    [Google Scholar]
  42. Bali, A. P. S., Fernandes, M., Choubey, S., & Goel, M. (2019). Comparative performance of machine learning algorithms for fake news detection. In Advances in Computing and Data Sciences: Third International Conference, ICACDS 2019, Ghaziabad, India, April 12–13, 2019, Revised Selected Papers, Part II 3 (pp. 420-430). Springer Singapore.
    [Google Scholar]
  43. Faustini, P. H. A., & Covoes, T. F. (2020). Fake news detection in multiple platforms and languages. Expert Systems with Applications, 158, 113503.
    [CrossRef]   [Google Scholar]
  44. Bangyal, W. H., Qasim, R., Rehman, N. U., Ahmad, Z., Dar, H., Rukhsar, L., ... & Ahmad, J. (2021). Detection of Fake News Text Classification on COVID‐19 Using Deep Learning Approaches. Computational and mathematical methods in medicine, 2021(1), 5514220.
    [CrossRef]   [Google Scholar]
  45. Wu, J., Huang, C., & Chen, Y. (2020, October). Patent Text Classification Study Based on Bi-LSTM-A Model. In 2020 5th international conference on control, Robotics and Cybernetics (CRC) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  46. Ganesh, P., Priya, L., & Nandakumar, R. (2021, June). Fake news detection-a comparative study of advanced ensemble approaches. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1003-1008). IEEE.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Nadeem, M., Abbas, P., Zhang, W., Rafique, S., & Iqbal, S. (2024). Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework. ICCK Transactions on Intelligent Systematics, 1(3), 203–214. https://doi.org/10.62762/TIS.2024.461943
Export Citation
RIS Format
Compatible with EndNote, Zotero, Mendeley, and other reference managers
RIS format data for reference managers
TY  - JOUR
AU  - Nadeem, Muhammad
AU  - Abbas, Parchamdar
AU  - Zhang, Wei
AU  - Rafique, Sumaira
AU  - Iqbal, Sundas
PY  - 2024
DA  - 2024/12/12
TI  - Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 3
SP  - 203
EP  - 214
DO  - 10.62762/TIS.2024.461943
UR  - https://www.icck.org/article/abs/TIS.2024.461943
KW  - fake news
KW  - natural language processing
KW  - statistical technique
KW  - machine learning
KW  - maximum likelihood estimation
KW  - social media
AB  - The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
BibTeX format data for LaTeX and reference managers
@article{Nadeem2024Enhancing,
  author = {Muhammad Nadeem and Parchamdar Abbas and Wei Zhang and Sumaira Rafique and Sundas Iqbal},
  title = {Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {3},
  pages = {203-214},
  doi = {10.62762/TIS.2024.461943},
  url = {https://www.icck.org/article/abs/TIS.2024.461943},
  abstract = {The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95\% accuracy and 92\% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.},
  keywords = {fake news, natural language processing, statistical technique, machine learning, maximum likelihood estimation, social media},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}
Article Metrics
Citations:

Google Scholar
8

Crossref

4

Scopus

2

Web of Science

3
Article Access Statistics:
Views: 7602
PDF Downloads: 1758
Publisher's Note
ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and Permissions
Institute of Central Computation and Knowledge (ICCK) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ICCK Transactions on Intelligent Systematics

ICCK Transactions on Intelligent Systematics

ISSN: 3068-5079 (Online) | ISSN: 3069-003X (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/icck/