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        <title>Mitigation of Diachronic Bias in Fake News Detection Dataset</title>
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    <name type="personal">
        <namePart type="given">Taichi</namePart>
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    <name type="personal">
        <namePart type="given">Eiji</namePart>
        <namePart type="family">Aramaki</namePart>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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    <abstract>Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.21</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Mitigation of Diachronic Bias in Fake News Detection Dataset
%A Murayama, Taichi
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F murayama-etal-2021-mitigation
%X Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.
%R 10.18653/v1/2021.wnut-1.21
%U https://aclanthology.org/2021.wnut-1.21
%U https://doi.org/10.18653/v1/2021.wnut-1.21
%P 182-188

Markdown (Informal)

[Mitigation of Diachronic Bias in Fake News Detection Dataset](https://aclanthology.org/2021.wnut-1.21) (Murayama et al., WNUT 2021)

• Mitigation of Diachronic Bias in Fake News Detection Dataset (Murayama et al., WNUT 2021)

ACL

• Taichi Murayama, Shoko Wakamiya, and Eiji Aramaki. 2021. Mitigation of Diachronic Bias in Fake News Detection Dataset. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 182–188, Online. Association for Computational Linguistics.