用分而治之策略结合内容与上下文特征，快速识别社交媒体假新闻。
Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
- 分而治之框架融合文本语言特征与词嵌入模型
- 在三个数据集上准确率最高达97.88%
- 适合需要实时反假新闻的平台或研究者
随着技术与互联网的快速发展，社交媒体上的假新闻泛滥已成为严重问题，导致广泛误导性信息传播并可能引发社会危害。传统人工核验方法通常过慢，难以阻止虚假信息扩散。因此，迫切需要快速、自动化的假新闻检测手段。本文提出DaCFake，一种基于分而治之策略的新型假新闻检测模型，结合内容与上下文特征。该方法从新闻文章中提取超过80个语言学特征，并与连续词袋或跳跃语法模型融合以提升检测精度。在Kaggle、McIntire+PolitiFact和Reuter三个数据集上，模型分别达到97.88%、96.05%和97.32%的准确率。此外，采用十折交叉验证进一步增强模型鲁棒性与准确性。结果表明，DaCFake在假新闻早期检测中表现优异，为遏制社交媒体上虚假信息传播提供了有效方案。
原文摘要 · Abstract (English)
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.
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