Work

A Multilingual Fake News Detection and Fact-Checking System Using NLP and Machine Learning

Year: 2026

Type: conference-paper

Abstract: This paper presents a machine learning based approach for detecting fake news using natural language processing techniques. The proposed system applies text preprocessing, TF-IDF feature extraction, and supervised classification algorithms such as Logistic Regression, Naive Bayes, and Passive-Aggressive Classifier to classify news articles as real or fake. Experimental results demonstrate that traditional machine learning models can achieve reliable performance on benchmark datasets. The study highlights the effectiveness of lightweight NLP models for fake news detection and can be extended for real-world misinformation analysis.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18496841

Authors: CHAUDHARY, SINGH, SONI, KUMAR

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7128022867

FWCI:

Cites:

Cited by:

Related to: