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        <title>True or False? Detecting False Information on Social Media Using Graph Neural Networks</title>
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        <namePart type="given">Samyo</namePart>
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    <abstract>In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.</abstract>
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%0 Conference Proceedings
%T True or False? Detecting False Information on Social Media Using Graph Neural Networks
%A Rode-Hasinger, Samyo
%A Kruspe, Anna
%A Zhu, Xiao Xiang
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F rode-hasinger-etal-2022-true
%X In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.
%U https://aclanthology.org/2022.wnut-1.24
%P 222-229

Markdown (Informal)

[True or False? Detecting False Information on Social Media Using Graph Neural Networks](https://aclanthology.org/2022.wnut-1.24) (Rode-Hasinger et al., WNUT 2022)

• True or False? Detecting False Information on Social Media Using Graph Neural Networks (Rode-Hasinger et al., WNUT 2022)

ACL

• Samyo Rode-Hasinger, Anna Kruspe, and Xiao Xiang Zhu. 2022. True or False? Detecting False Information on Social Media Using Graph Neural Networks. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 222–229, Gyeongju, Republic of Korea. Association for Computational Linguistics.