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User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis.

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Users sought health information and emotional support from chatbots during the COVID-19 pandemic. Analysis revealed cultural differences in user sentiment towards pandemic-related topics discussed with chatbots.

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Area of Science:

  • Public Health
  • Human-Computer Interaction
  • Computational Social Science

Background:

  • Chatbots show promise for public health initiatives, but user interactions during the COVID-19 pandemic remain under-researched.
  • Understanding these interactions is vital for developing effective health emergency support services.

Purpose of the Study:

  • To investigate COVID-19 pandemic-related topics discussed by online users with a social chatbot.
  • To compare user sentiment across five culturally diverse countries regarding these discussions.

Main Methods:

  • Analysis of 19,782 COVID-19 related conversation utterances from SimSimi (a social chatbot) between 2020-2021.
  • Natural language processing for topic identification and sentiment analysis across the United States, United Kingdom, Canada, Malaysia, and the Philippines.

Main Results:

  • Identified 18 topics across five themes: questions about COVID-19, preventive behaviors, outbreak details, health impacts, and pandemic life.
  • Users treated chatbots as information sources for health queries and for emotional connection during lockdowns.
  • Negative sentiment was associated with masks, lockdowns, and pandemic worries; positive sentiment dominated small talk. US users expressed more negative sentiment than Asian users.

Conclusions:

  • User-chatbot interactions reveal informational and emotional needs during health crises.
  • Chatbots have potential for delivering accurate health information and emotional support.
  • Future research should explore support strategies aligned with public health policies.