Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916825961332736 |
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| author | Seidlmayer, Eva Galke, Lukas Förstner, Konrad U. |
| author_facet | Seidlmayer, Eva Galke, Lukas Förstner, Konrad U. |
| contents | Disseminators of disinformation often seek to attract attention or evoke emotions - typically to gain influence or generate revenue - resulting in distinctive rhetorical patterns that can be exploited by machine learning models. In this study, we explore linguistic and rhetorical features as proxies for distinguishing disinformative texts from other health and life-science text genres, applying both large language models and classical machine learning classifiers. Given the limitations of existing datasets, which mainly focus on fact checking misinformation, we introduce Four Shades of Life Sciences (FSoLS): a novel, labeled corpus of 2,603 texts on 14 life-science topics, retrieved from 17 diverse sources and classified into four categories of life science publications. The source code for replicating, and updating the dataset is available on GitHub: https://github.com/EvaSeidlmayer/FourShadesofLifeSciences |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_03488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences Seidlmayer, Eva Galke, Lukas Förstner, Konrad U. Computation and Language Disseminators of disinformation often seek to attract attention or evoke emotions - typically to gain influence or generate revenue - resulting in distinctive rhetorical patterns that can be exploited by machine learning models. In this study, we explore linguistic and rhetorical features as proxies for distinguishing disinformative texts from other health and life-science text genres, applying both large language models and classical machine learning classifiers. Given the limitations of existing datasets, which mainly focus on fact checking misinformation, we introduce Four Shades of Life Sciences (FSoLS): a novel, labeled corpus of 2,603 texts on 14 life-science topics, retrieved from 17 diverse sources and classified into four categories of life science publications. The source code for replicating, and updating the dataset is available on GitHub: https://github.com/EvaSeidlmayer/FourShadesofLifeSciences |
| title | Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.03488 |