ArticleFrontiers in public health2023
Linguistic indicators for predicting the veracity of online health rumors.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Hedging, ambiguity, and the rejection of misinformation: evidence from Chinese readers.Frontiers in psychology · 2026Article
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2 authors.
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Abstract
This study aims to examine the role of language in discerning the authenticity of online health rumors. To achieve this goal, it specifically focuses on analyzing five categories of linguistic indicators: (1) emotional language characterized by sentiment words, sensory words, and continuous punctuations, (2) exaggerated language defined by the presence of extreme numbers and extreme adverbs, (3) personalized language denoted by first-person pronouns, (4) unprofessional language represented by typographical errors, and (5) linkage language marked by inclusion of hyperlinks. To conduct the investigation, a dataset consisting of 1,500 information items was utilized. The dataset exhibited a distribution pattern wherein 20% of the information was verified to be true, while the remaining 80% was categorized as rumors. These items were sourced from two prominent rumor-clarification websites in China. A binomial logistic regression was used for data analysis to determine whether the language used in an online health rumor could predict its authenticity. The results of the analysis showed that the presence of sentiment words, continuous punctuation marks, extreme numbers and adverbs in an online health rumor could predict its authenticity. Personalized language, typographical errors, and hyperlinks were also found to be useful indicators for identifying health rumors using linguistic indicators. These results provide valuable insights for identifying health rumors using language-based features and could help individuals and organizations better understand the credibility of online health information.
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