Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909347074801664 |
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| author | Liu, Yanchen Ma, Mingyu Derek Qin, Wenna Zhou, Azure Chen, Jiaao Shi, Weiyan Wang, Wei Yang, Diyi |
| author_facet | Liu, Yanchen Ma, Mingyu Derek Qin, Wenna Zhou, Azure Chen, Jiaao Shi, Weiyan Wang, Wei Yang, Diyi |
| contents | 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 model users' latent susceptibility levels. As shown in previous research, 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 domain, 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 psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09630 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach Liu, Yanchen Ma, Mingyu Derek Qin, Wenna Zhou, Azure Chen, Jiaao Shi, Weiyan Wang, Wei Yang, Diyi Computation and Language Computers and Society Social and Information Networks 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 model users' latent susceptibility levels. As shown in previous research, 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 domain, 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 psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
| title | Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach |
| topic | Computation and Language Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2311.09630 |