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

2025-08-22

AI Summary

To address the rapid propagation of fake news on social media and the latency inherent in traditional fact-checking, this paper proposes an automated detection framework based on a “divide-and-conquer” strategy. The method innovatively fuses over 80 linguistic features with CBOW and Skip-gram word embeddings to jointly model news content and contextual information; ten-fold cross-validation is employed to enhance generalizability and enable early detection. Evaluated on three benchmark datasets—Kaggle, McIntire+PolitiFact, and Reuters—the framework achieves accuracies of 97.88%, 96.05%, and 97.32%, respectively, outperforming state-of-the-art approaches. Key contributions include: (1) a highly efficient and scalable multi-feature fusion architecture; (2) empirical validation of cross-dataset robustness; and (3) a practical, deployable technical pathway for real-time misinformation mitigation.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Automated detection of fake news on social media
• Combining content and context features for accuracy
• Early identification to prevent misinformation spread

Innovation

• Divide and conquer strategy combining content and context
• Extracts over eighty linguistic features from articles
• Integrates with continuous bag of words or skipgram