Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events
13th International Conference on Language Resources and Evaluation Conference, LREC 2022
; : 4135-4141, 2022.
Article
in English
| Scopus | ID: covidwho-2167407
ABSTRACT
The COVID-19 pandemic and other global health events are unfortunately excellent environments for the creation and spread of misinformation, and the language associated with health misinformation may be typified by unique patterns and linguistic markers. Allowing health misinformation to spread unchecked can have devastating ripple effects;however, detecting and stopping its spread requires careful analysis of these linguistic characteristics at scale. We analyze prior investigations focusing on health misinformation, associated datasets, and detection of misinformation during health crises. We also introduce a novel dataset designed for analyzing such phenomena, comprised of 2.8 million news articles and social media posts spanning the early 1900s to the present. Our annotation guidelines result in strong agreement between independent annotators. We describe our methods for collecting this data and follow this with a thorough analysis of the themes and linguistic features that appear in information versus misinformation. Finally, we demonstrate a proof-of-concept misinformation detection task to establish dataset validity, achieving a strong performance benchmark (accuracy = 75%;F1 = 0.7). © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Year:
2022
Document Type:
Article
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