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  - 

@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}
}