Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media

Fuente: arXiv

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Bibliographic Details
Main Authors:	Jain, Mayank Kumar, Gopalani, Dinesh, Meena, Yogesh Kumar, Jain, Nishant
Format:	Preprint
Published:	2025
Subjects:	Social and Information Networks Machine Learning
Online Access:	Acceder al recurso
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author	Jain, Mayank Kumar Gopalani, Dinesh Meena, Yogesh Kumar Jain, Nishant
author_facet	Jain, Mayank Kumar Gopalani, Dinesh Meena, Yogesh Kumar Jain, Nishant
contents	With the rapid evolution of technology and the Internet, the proliferation of fake news on social media has become a critical issue, leading to widespread misinformation that can cause societal harm. Traditional fact checking methods are often too slow to prevent the dissemination of false information. Therefore, the need for rapid, automated detection of fake news is paramount. We introduce DaCFake, a novel fake news detection model using a divide and conquer strategy that combines content and context based features. Our approach extracts over eighty linguistic features from news articles and integrates them with either a continuous bag of words or a skipgram model for enhanced detection accuracy. We evaluated the performance of DaCFake on three datasets including Kaggle, McIntire + PolitiFact, and Reuter achieving impressive accuracy rates of 97.88%, 96.05%, and 97.32%, respectively. Additionally, we employed a ten-fold cross validation to further enhance the model's robustness and accuracy. These results highlight the effectiveness of DaCFake in early detection of fake news, offering a promising solution to curb misinformation on social media platforms.
format	Preprint
id	arxiv_https___arxiv_org_abs_2508_16223
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media Jain, Mayank Kumar Gopalani, Dinesh Meena, Yogesh Kumar Jain, Nishant Social and Information Networks Machine Learning With the rapid evolution of technology and the Internet, the proliferation of fake news on social media has become a critical issue, leading to widespread misinformation that can cause societal harm. Traditional fact checking methods are often too slow to prevent the dissemination of false information. Therefore, the need for rapid, automated detection of fake news is paramount. We introduce DaCFake, a novel fake news detection model using a divide and conquer strategy that combines content and context based features. Our approach extracts over eighty linguistic features from news articles and integrates them with either a continuous bag of words or a skipgram model for enhanced detection accuracy. We evaluated the performance of DaCFake on three datasets including Kaggle, McIntire + PolitiFact, and Reuter achieving impressive accuracy rates of 97.88%, 96.05%, and 97.32%, respectively. Additionally, we employed a ten-fold cross validation to further enhance the model's robustness and accuracy. These results highlight the effectiveness of DaCFake in early detection of fake news, offering a promising solution to curb misinformation on social media platforms.
title	Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
topic	Social and Information Networks Machine Learning
url	https://arxiv.org/abs/2508.16223