@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
%A Fung, Yi
%A Thomas, Christopher
%A Gangi Reddy, Revanth
%A Polisetty, Sandeep
%A Ji, Heng
%A Chang, Shih-Fu
%A McKeown, Kathleen
%A Bansal, Mohit
%A Sil, Avi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F fung-etal-2021-infosurgeon
%X 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.
%R 10.18653/v1/2021.acl-long.133
%U https://aclanthology.org/2021.acl-long.133
%U https://doi.org/10.18653/v1/2021.acl-long.133
%P 1683-1698
Markdown (Informal)
[InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection](https://aclanthology.org/2021.acl-long.133) (Fung et al., ACL-IJCNLP 2021)
ACL
- Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, and Avi Sil. 2021. InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection. In 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), pages 1683–1698, Online. Association for Computational Linguistics.