International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Comparing Machine Learning and Deep Learning Approaches for Fake News Detection: Challenges, Methods, and Where We Go from Here |
|---|---|
| ABSTRACT | Fake news has become one of the serious challenges of the digital age. It is not just an academic issue; it poses a genuine threat to how democracies operate and how people make choices about their health, safety, and politics. The problem isn’t new, but its scale is. Millions of articles are published every hour on social platforms, and traditional fact-checking cannot keep up. This is why automated detection is an urgent research focus. This paper reviews six important studies on fake news detection. It covers methods that range from traditional classifiers to the latest transformer models. The techniques examined include Support Vector Machines (SVM), Naive Bayes, Logistic Regression, XGBoost, CNNs, LSTMs, BERT, RoBERTa, and GPT. These methods were tested on benchmark datasets such as ISOT, WELFake, LIAR, and FakeNewsNet. After looking at what each study contributes and where it has limitations, four common issues appear: nearly all models only work in English, almost none handle images along with text, static models decline as manipulation tactics change, and black-box deep learning systems seldom explain their decisions in ways a human can understand. To fill these gaps, we propose the Hybrid Adaptive Multimodal Framework (HAMF). This system combines fine-tuned multilingual transformer models, graph-based source credibility analysis, and explainability tools into a solution that is more accurate and, importantly, trustworthy. |
| AUTHOR | PROF. USHA K, CHAITRA B S, CHANDANA S, CHANDRALEKHA M S, HARSHITHA G K, HEMA GM Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405011 |
| pdf/11_Comparing Machine Learning and Deep Learning Approaches for Fake News Detection Challenges, Methods.pdf | |
| KEYWORDS | |
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