Addressing the spread of health-related misinformation on social networks: an opinion article

Polyzou, Maria; Kiefer, David M.; Baraliakos, Xenofon; Sewerin, Philipp

doi:10.3389/fmed.2023.1167033

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…While such mediums allow easy dissemination of breakthroughs, health insights etc., they also can be used for spreading of false information. Such false information, especially when related to medical and health areas, can quickly proliferate and even lead to misleading product claims on e-commerce websites for driving sales 1 . These claims often directly influence consumer purchasing behaviors.…”

Section: Introductionmentioning

confidence: 99%

MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference

Karkera,

Ghosh,

Escames

et al. 2024

Preprint

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With the explosion of health related information in mainstream discourse, distinguishing accurate health-related claims from misinformation is important. Using computational tools and algorithms to help is key. Our focus in this paper is on the hormone Melatonin which is claimed to have broad health benefits and largely sold as a supplement. This paper introduces 'MelAnalyze,' a framework for using generative and transformer-based deep learning models adapted as a natural language inference (NLI) task, to semi-automate the fact-checking of general melatonin claims. MelAnalyze is built upon a comprehensive collection of melatonin-related scientific abstracts from PubMed for validation. The framework incorporates components for precise extraction of information from scientific literature, semantic similarity and NLI. At its core, MelAnalyze leverages pre trained NLI models that are fine-tuned on melatonin-specific claims along with semantic search based on vectorized representation of the articles. The best models, fine-tuned on LLaMA1 and RoBERTa, attain good precision, recall, and F1-scores of approximately 0.92. We also introduce a user-friendly web-based tool for fact-checking algorithm evaluation and use. In summary, we show MelAnalyze's role in empowering users and researchers to assess melatonin-related claims using evidence-based decision-making.

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Section: Introductionmentioning

confidence: 99%

MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference

Karkera,

Ghosh,

Escames

et al. 2024

Preprint

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“…Widespread misinformation, disinformation, and malinformation [3] related to diet and nutrition have been fueled by various factors, including the ease of disseminating information through digital platforms and the vast array of sources available to individuals [4][5][6][7]. This abundance of information, however, comes with inherent challenges, as the accuracy and reliability of the information are not guaranteed [8,9].…”

Section: Introductionmentioning

confidence: 99%

Diet-Nutrition Information Seeking, Source Trustworthiness, and Eating Behavior Changes: An International Web-Based Survey

Ruani,

Reiss,

Kalea

2023

Nutrients

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To understand the extent to which different sources of diet and nutrition information are sought, trusted, and relied upon for making dietary changes, the present international web-based survey study gauged participants’ (n = 3419) diet-nutrition information-seeking behaviors from 22 interpersonal and general sources with varying quality, trust levels in these sources, and reliance on each source for making dietary changes. Qualitative insights were also captured regarding trustworthiness formation. The results revealed a disconnect between source popularity and perceived trustworthiness. While nutrition–health websites, Google–Internet searches, and diet–health books were most commonly consulted, participants placed the highest level of trust in nutrition scientists, nutrition professionals, and scientific journals, suggesting that frequent information seeking from a subpar source may not be a reliable predictor of the level of trust assigned to it. Although the frequency of source-seeking behaviors and source trustworthiness both contributed to dietary changes, the latter appeared to have a more pronounced influence. When a source was less trusted, there was a reduced likelihood of relying on it for changing diet. Additionally, source seeking may not always translate into effective dietary change, as shown by the less strong correlation between the two. These associations significantly differed depending on the source.

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Addressing the spread of health-related misinformation on social networks: an opinion article

Cited by 2 publications

References 22 publications

MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference

MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference

Diet-Nutrition Information Seeking, Source Trustworthiness, and Eating Behavior Changes: An International Web-Based Survey

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