@inproceedings{32e9a87bcd7a435fa9dea5ebdda34047,
title = "dEFEND: A system for explainable fake news detection",
abstract = "Despite recent advancements in computationally detecting fake news, we argue that a critical missing piece be the explainability of such detection-i.e., why a particular piece of news is detected as fake-and propose to exploit rich information in users' comments on social media to infer the authenticity of news. In this demo paper, we present our system for an explainable fake news detection called dEFEND, which can detect the authenticity of a piece of news while identifying user comments that can explain why the news is fake or real. Our solution develops 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. The system is publicly accessible.",
keywords = "Deep learning, Explainable machine Learning, Fake news",
author = "Limeng Cui and Kai Shu and Suhang Wang and Dongwon Lee and Huan Liu",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3357862",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "2961--2964",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
note = "28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
}