@inproceedings{liu-etal-2024-decoding,
title = "Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach",
author = "Liu, Yanchen and
Ma, Mingyu and
Qin, Wenna and
Zhou, Azure and
Chen, Jiaao and
Shi, Weiyan and
Wang, Wei and
Yang, Diyi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.846",
pages = "15178--15194",
abstract = "Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals{'} mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. To address these limitations, in this work, we propose a computational approach to efficiently model users{'} latent susceptibility levels. As shown in previous work, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people{'}s reposting behavior on social media. To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people{'}s sharing behavior. Using COVID-19 as a testbed, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach. Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors. Our analysis reveals that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation, and shows that susceptibility is unevenly distributed across different professional and geographical backgrounds.",
}
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<abstract>Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals’ mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. To address these limitations, in this work, we propose a computational approach to efficiently model users’ latent susceptibility levels. As shown in previous work, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people’s reposting behavior on social media. To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people’s sharing behavior. Using COVID-19 as a testbed, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach. Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors. Our analysis reveals that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation, and shows that susceptibility is unevenly distributed across different professional and geographical backgrounds.</abstract>
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%0 Conference Proceedings
%T Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
%A Liu, Yanchen
%A Ma, Mingyu
%A Qin, Wenna
%A Zhou, Azure
%A Chen, Jiaao
%A Shi, Weiyan
%A Wang, Wei
%A Yang, Diyi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-decoding
%X Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals’ mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. To address these limitations, in this work, we propose a computational approach to efficiently model users’ latent susceptibility levels. As shown in previous work, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people’s reposting behavior on social media. To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people’s sharing behavior. Using COVID-19 as a testbed, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach. Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors. Our analysis reveals that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation, and shows that susceptibility is unevenly distributed across different professional and geographical backgrounds.
%U https://aclanthology.org/2024.emnlp-main.846
%P 15178-15194
Markdown (Informal)
[Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach](https://aclanthology.org/2024.emnlp-main.846) (Liu et al., EMNLP 2024)
ACL
- Yanchen Liu, Mingyu Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, and Diyi Yang. 2024. Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15178–15194, Miami, Florida, USA. Association for Computational Linguistics.