TY  - JOUR
AU  - Zeeshan, Hafiz Muhammad
AU  - Ullah, Inam
AU  - Yousaf, Furkan
AU  - Sharafian, Amin
AU  - Heyat, Md Belal Bin
AU  - Saqib, Shazia
AU  - Rahman, Atta Ur
PY  - 2025
DA  - 2025/01/04
TI  - A Machine Learning-Based Scientometric Evaluation for Fake News Detection
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 2
IS  - 1
SP  - 38
EP  - 48
DO  - 10.62762/TIS.2024.564569
UR  - https://www.icck.org/article/abs/TIS.2024.564569
KW  - machine learning
KW  - fake news detection
KW  - bibliometric analysis
KW  - information ecosystem
KW  - intelligent systems
AB  - Fake news detection has emerged as a critical challenge in the modern information ecosystem, where the rapid proliferation of misinformation threatens democratic processes, public health, and societal stability. Machine learning (ML)-based approaches have demonstrated significant promise in automatically identifying and classifying misleading information across diverse platforms. This study presents a comprehensive scientometric and systematic review of ML-based fake news detection research, drawing on 649 peer-reviewed articles indexed in the Web of Science database (1991--2023). Using bibliometric tools including R-Bibliometrix and VOSviewer, we systematically evaluate publication trends, collaborative networks, thematic evolution, and the relative effectiveness of key ML paradigms---including classical classifiers (SVM, Naive Bayes), deep learning architectures (LSTM, CNN), and transformer-based models (BERT, RoBERTa)---as reflected in the literature. Our analysis reveals a significant surge in research activity post-2016, driven by electoral misinformation and the COVID-19 infodemic, with transformer-based models increasingly dominating the field due to their superior contextual understanding. The findings identify critical research gaps, including limited cross-lingual detection capabilities and insufficient attention to multimodal fake news involving deepfakes. This review provides actionable insights for researchers and practitioners seeking to advance intelligent systems for misinformation detection.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Zeeshan2025A,
  author = {Hafiz Muhammad Zeeshan and Inam Ullah and Furkan Yousaf and Amin Sharafian and Md Belal Bin Heyat and Shazia Saqib and Atta Ur Rahman},
  title = {A Machine Learning-Based Scientometric Evaluation for Fake News Detection},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {38-48},
  doi = {10.62762/TIS.2024.564569},
  url = {https://www.icck.org/article/abs/TIS.2024.564569},
  abstract = {Fake news detection has emerged as a critical challenge in the modern information ecosystem, where the rapid proliferation of misinformation threatens democratic processes, public health, and societal stability. Machine learning (ML)-based approaches have demonstrated significant promise in automatically identifying and classifying misleading information across diverse platforms. This study presents a comprehensive scientometric and systematic review of ML-based fake news detection research, drawing on 649 peer-reviewed articles indexed in the Web of Science database (1991--2023). Using bibliometric tools including R-Bibliometrix and VOSviewer, we systematically evaluate publication trends, collaborative networks, thematic evolution, and the relative effectiveness of key ML paradigms---including classical classifiers (SVM, Naive Bayes), deep learning architectures (LSTM, CNN), and transformer-based models (BERT, RoBERTa)---as reflected in the literature. Our analysis reveals a significant surge in research activity post-2016, driven by electoral misinformation and the COVID-19 infodemic, with transformer-based models increasingly dominating the field due to their superior contextual understanding. The findings identify critical research gaps, including limited cross-lingual detection capabilities and insufficient attention to multimodal fake news involving deepfakes. This review provides actionable insights for researchers and practitioners seeking to advance intelligent systems for misinformation detection.},
  keywords = {machine learning, fake news detection, bibliometric analysis, information ecosystem, intelligent systems},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}