Nepali Fake News Detection Using Machine Learning Algorithms

The Internet's rapid development makes it possible for information to spread quickly through websitesor social networks. Fake or unverified news spreads on social media and reaches thousands of userswithout anyone questioning its veracity. Misinformation or fabricated news that spreads on social mediawith the intention of harming a person, group, or agency is known as fake news. Various machine-learningtechniques have been used to distinguish fake news from real. In this paper, we tried to achieve this usingmachine learning techniques and natural language processing. We use frequency-inverse documentfrequency as a feature extraction method and logistic regression, support vector machine and Naive Bayesas the classifier. We use a dataset with labels for fake and real news to train our model.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-11
DOI
https://doi.org/10.5281/zenodo.16793592
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

Nepali Fake News Detection Using Machine Learning Algorithms

Zenodo (CERN European Organization for Nuclear Research)
Misinformation and Its Impacts
article

Nepali Fake News Detection Using Machine Learning Algorithms

article en

Abstract

The Internet's rapid development makes it possible for information to spread quickly through websitesor social networks. Fake or unverified news spreads on social media and reaches thousands of userswithout anyone questioning its veracity. Misinformation or fabricated news that spreads on social mediawith the intention of harming a person, group, or agency is known as fake news. Various machine-learningtechniques have been used to distinguish fake news from real. In this paper, we tried to achieve this usingmachine learning techniques and natural language processing. We use frequency-inverse documentfrequency as a feature extraction method and logistic regression, support vector machine and Naive Bayesas the classifier. We use a dataset with labels for fake and real news to train our model.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 43%
Misinformation and Its Impacts
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