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Data-driven analytics of COVID-19 'infodemic'.

Minyu Wan1, Qi Su2, Rong Xiang3

  • 1Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China.

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Summary

This study analyzes COVID-19 misinformation, revealing language patterns that exploit negative sentiment and multimedia to spread. Understanding these linguistic triggers is key to combating the infodemic.

Keywords:
COVID-19 InfodemicEvaluation–potency–activityInformation credibilityLinguistic featuresMisinformation

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Area of Science:

  • Computational Linguistics
  • Public Health Communication
  • Psycholinguistics

Background:

  • The COVID-19 pandemic was accompanied by a significant infodemic, challenging information credibility.
  • Existing research on misinformation often overlooks linguistic nuances and consumer psycho-social behavior.
  • Current fact-checking and labeling models lack a deep understanding of language characteristics.

Purpose of the Study:

  • To identify lexical and grammatical features of COVID-19 misinformation.
  • To analyze psycho-linguistic triggers (sentiment, power, activity) using Affective Control Theory.
  • To develop feature indexing for anti-infodemic modeling.

Main Methods:

  • Data-driven analysis of COVID-19 misinformation content.
  • Linguistic feature extraction, focusing on sentiment, power, and activity.
  • Application of Affective Control Theory for psycho-linguistic interpretation.
  • Development of feature indexing for misinformation detection models.

Main Results:

  • Misinformation exhibits distinct language patterns, favoring evaluative terms and multimedia.
  • Negative sentiment is a prominent feature used to engage audiences.
  • Appeals to sympathy and emotional triggers effectively encourage information sharing.

Conclusions:

  • COVID-19 misinformation employs specific linguistic strategies to evoke emotional responses and promote spread.
  • Understanding these psycho-linguistic triggers is crucial for effective anti-infodemic interventions.
  • Feature indexing based on these linguistic patterns can enhance misinformation modeling.