The dissemination of fake news significantly affects personal reputation and
public trust. Recently, fake news detection has attracted tremendous attention,
and previous studies mainly focused on finding clues from news content or
diffusion path. However, the required features of previous models are often
unavailable or insufficient in early detection scenarios, resulting in poor
performance. Thus, early fake news detection remains a tough challenge.
Intuitively, the news from trusted and authoritative sources or shared by many
users with a good reputation is more reliable than other news. Using the
credibility of publishers and users as prior weakly supervised information, we
can quickly locate fake news in massive news and detect them in the early
stages of dissemination.
In this paper, we propose a novel Structure-aware Multi-head Attention
Network (SMAN), which combines the news content, publishing, and reposting
relations of publishers and users, to jointly optimize the fake news detection
and credibility prediction tasks. In this way, we can explicitly exploit the
credibility of publishers and users for early fake news detection. We conducted
experiments on three real-world datasets, and the results show that SMAN can
detect fake news in 4 hours with an accuracy of over 91%, which is much faster
than the state-of-the-art models.

本文提出了一种新的结构感知多头注意力网络（SMAN），该网络通过联合优化假新闻检测和可信度预测任务，明确地利用出版商和用户的可信度进行早期假新闻检测，实验结果表明 SMAN 可以在 4 小时内以超过 91% 的准确率检测出假新闻，比现有模型快得多。