Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences
This work addresses the challenge of identifying disinformation in health and life sciences for researchers and practitioners, though it is incremental as it focuses on dataset creation rather than novel detection methods.
The authors tackled the problem of detecting disinformation in life sciences by introducing a new dataset, Four Shades of Life Sciences (FSoLS), which includes 2,603 texts across 14 topics and four categories, enabling the use of machine learning models to distinguish disinformative content based on linguistic features.
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