dEFEND: Explainable Fake News Detection

被引：403

作者：

Shu, Kai ^{[1
]}

Cui, Limeng ^{[2
]}

Wang, Suhang ^{[2
]}

Lee, Dongwon ^{[2
]}

Liu, Huan ^{[1
]}

机构：

[1] Arizona State Univ, Tempe, AZ 85201 USA

[2] Penn State Univ, University Pk, PA 16802 USA

来源：

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2019年

关键词：

Fake news; explainable machine learning; social network;

D O I：

10.1145/3292500.3330935

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, to mitigate the problem of fake news, computational detection of fake news has been studied, producing some promising early results. While important, however, we argue that a critical missing piece of the study be the explainability of such detection, i.e., why a particular piece of news is detected as fake. In this paper, therefore, we study the explainable detection of fake news. We develop a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. We conduct extensive experiments on real world datasets and demonstrate that the proposed method not only significantly outperforms 7 state-of-the-art fake news detection methods by at least 5.33% in F1-score, but also (concurrently) identifies top-k user comments that explain why a news piece is fake, better than baselines by 28.2% in NDCG and 30.7% in Precision.

引用

页码：395 / 405

页数：11