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        <title>From Fake to Hyperpartisan News Detection Using Domain Adaptation</title>
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    <name type="personal">
        <namePart type="given">Răzvan-Alexandru</namePart>
        <namePart type="family">Smădu</namePart>
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    <name type="personal">
        <namePart type="given">Sebastian-Vasile</namePart>
        <namePart type="family">Echim</namePart>
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    <name type="personal">
        <namePart type="given">Dumitru-Clementin</namePart>
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    <name type="personal">
        <namePart type="given">Iuliana</namePart>
        <namePart type="family">Marin</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Florin</namePart>
        <namePart type="family">Pop</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.117</url>
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        <date>2023-09</date>
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            <start>1095</start>
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%0 Conference Proceedings
%T From Fake to Hyperpartisan News Detection Using Domain Adaptation
%A Smădu, Răzvan-Alexandru
%A Echim, Sebastian-Vasile
%A Cercel, Dumitru-Clementin
%A Marin, Iuliana
%A Pop, Florin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F smadu-etal-2023-fake
%X Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.
%U https://aclanthology.org/2023.ranlp-1.117
%P 1095-1109

Markdown (Informal)

[From Fake to Hyperpartisan News Detection Using Domain Adaptation](https://aclanthology.org/2023.ranlp-1.117) (Smădu et al., RANLP 2023)

• From Fake to Hyperpartisan News Detection Using Domain Adaptation (Smădu et al., RANLP 2023)

ACL

• Răzvan-Alexandru Smădu, Sebastian-Vasile Echim, Dumitru-Clementin Cercel, Iuliana Marin, and Florin Pop. 2023. From Fake to Hyperpartisan News Detection Using Domain Adaptation. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1095–1109, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.