Year: 2025
Type: article
Abstract: The rapid expansion of digital communication and social media, the spread of fake news has become a growing concern. Detecting and filtering out fake news is crucial, yet it remains a challenging task due to limited datasets and effective analysis techniques. This study presents a machine learning-based approach to detecting fake news. The system extracts textual features using Term Frequency-Inverse Document Frequency (T F- IDF) with bag-of-words and n-grams. A Support Vector Machine (SVM) classifier is then employed to differentiate between authentic and fake news. Additionally, a dataset containing both real and fake news articles is introduced for training the model. The results highlight the effectiveness of the proposed system in accurately identifying misinformation
Author: Himanshu Singh
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409376030