Risk Communication in Asian Countries: COVID-19 Discourse on Twitter

TLDR;

  • This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees.
  • The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics.
  • Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity.
  • Can official epidemic phases issued by governments reflect the online interaction patterns?
  • How to automatically divide topical phases based on a bottom-up approach?
  • What are the major topics corresponding to each topical phase?
  • What are the unique traits of the topical trends by country, and are there any notable online communicative characteristics that can be shared among those countries?

Data

Pipeline for Detecting Topical Phases then Extracting Topics

Figure 1. The pipeline of the topic analysis.

Basic Daily Trends

Figure 2. Daily trends on the Four countries: X-axis is dates and Y-axis is trends of # of tweets with log-scale.

South Korea

Figure 3. Daily trends on South Korea: start/end dates of the official epidemic phases (vertical dash lines), trends of # of tweets (blue lines), and that of # of the confirmed cases (red bars).

Iran

Figure 4. Daily trends on Iran: start/end dates of the official epidemic phases (vertical dash lines), trends of # of tweets (blue lines), and that of # of the confirmed cases (red bars).

Vietnam

Figure 5. Daily trends on Vietnam: start/end dates of the official epidemic phases (vertical dash lines), trends of # of tweets (blue lines), and that of # of the confirmed cases (red bars).

India

Figure 6. Daily trends on India: start/end dates of the official epidemic phases (vertical dash lines), trends of # of tweets (blue lines), and that of # of the confirmed cases (red bars).

Extracted Topical Trends

South Korea

Figure 7. Daily topical trends on South Korea: based on % (top), based on # of tweets (mid), and based on #of tweets country names mentioned (bottom).

Iran

Figure 8. Daily topical trends on Iran: based on % (top), based on # of tweets (mid), based on # of tweets country names mentioned (bottom).

Vietnam

Figure 9. Daily topical trends on Vietnam: based on % (top), based on # of tweets (mid), based on # of tweets country names mentioned (bottom).

India

Figure 10. Daily topical trends on India: based on % (top), based on # of tweets (mid), based on # of tweets country names mentioned (bottom).

Concluding Remark

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Institute for Basic Science

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IBS Data Science Group

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Institute for Basic Science

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