InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal
Abstract
To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.
BibTeX
@inproceedings{fung-etal-2021-infosurgeon,
title = "{I}nfo{S}urgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection",
author = "Fung, Yi and
Thomas, Christopher and
Gangi Reddy, Revanth and
Polisetty, Sandeep and
Ji, Heng and
Chang, Shih-Fu and
McKeown, Kathleen and
Bansal, Mohit and
Sil, Avi",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.133/",
doi = "10.18653/v1/2021.acl-long.133",
pages = "1683--1698"
}