@inproceedings{kelk-etal-2022-automatic,
title = "Automatic Fake News Detection: Are current models {``}fact-checking{''} or{``}gut-checking{''}?",
author = "Kelk, Ian and
Basseri, Benjamin and
Lee, Wee and
Qiu, Richard and
Tanner, Chris",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.fever-1.4",
doi = "10.18653/v1/2022.fever-1.4",
pages = "29--36",
abstract = "Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all {--} only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an {``}emotional attention{''} mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.",
}
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<abstract>Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all – only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an “emotional attention” mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.</abstract>
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%0 Conference Proceedings
%T Automatic Fake News Detection: Are current models “fact-checking” or“gut-checking”?
%A Kelk, Ian
%A Basseri, Benjamin
%A Lee, Wee
%A Qiu, Richard
%A Tanner, Chris
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kelk-etal-2022-automatic
%X Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all – only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an “emotional attention” mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.
%R 10.18653/v1/2022.fever-1.4
%U https://aclanthology.org/2022.fever-1.4
%U https://doi.org/10.18653/v1/2022.fever-1.4
%P 29-36
Markdown (Informal)
[Automatic Fake News Detection: Are current models “fact-checking” or“gut-checking”?](https://aclanthology.org/2022.fever-1.4) (Kelk et al., FEVER 2022)
ACL