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

TL;DR

DaCFake is a novel divide and conquer framework that effectively detects fake news on social media by combining linguistic features with word embedding models, achieving high accuracy across multiple datasets.

Contribution

Introduces DaCFake, a new fake news detection model that integrates content and context features using a divide and conquer approach for improved accuracy.

Findings

Achieved over 97% accuracy on three datasets

Effective in early fake news detection

Robust with ten-fold cross validation

Abstract

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.