Work

TF-IDF Based Fake News Classification: A Lightweight Machine Learning Study with Linear SVM Dominance

Year: 2026

Type: conference-paper

Abstract: Because of the prevalence of fake news in the current era that is spread in online media, this is causing fake news detection to be a huge challenge. This is because there is an abundance of fake news on social media channels, which misleads readers and provides them with fake information. Automatic fake news detection using machine learning algorithms has hence become an important research topic. In this research, we propose a model to detect fake news automatically. We make use of the Kaggle datasets that contain news data from both genuine sources and fake news websites. We perform TF-IDF vectorization, whereby we assign values to words based on their frequencies.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19843527

Authors: Singh, Sukhman, Kumar, Rakesh

Institution: GNA University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7156720956

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