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

Hyojin Chin, Gabriel Lima, Mingi Shin, Assem Zhunis, Chiyoung Cha, Junghoi Choi, Meeyoung Cha

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Background: Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people’s needs during a global health emergency. Objective: This study examined the COVID-19 pandemic–related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods: We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world’s largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19–related chats across countries. Results: Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: “Questions on COVID-19 asked to the chatbot” (30.6%), “Preventive behaviors” (25.3%), “Outbreak of COVID-19” (16.4%), “Physical and psychological impact of COVID-19” (16.0%), and “People and life in the pandemic” (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions: Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people’s informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.

Original languageEnglish
Article numbere40922
JournalJournal of Medical Internet Research
StatePublished - 2023

Bibliographical note

Funding Information:
HC, GL, MS, AZ, and MC were supported by the Institute for Basic Science, Republic of Korea (IBS-R029-C2). CC was supported by a National Research Foundation of Korea grant funded by the Ministry of Science and ICT (Information and Communications Technology) (2021R1A2C2008166). JC was funded by the Technology Development Program funded by the Ministry of SMEs (small and medium-sized enterprises) and Startups in 2022 (S3051210). The authors thank SimSimi for providing data for research purposes.

Publisher Copyright:
©Hyojin Chin, Gabriel Lima, Mingi Shin, Assem Zhunis, Chiyoung Cha, Junghoi Choi, Meeyoung Cha.


  • COVID-19
  • chatbot
  • conversational agent
  • discourse
  • global health
  • health information
  • infodemiology
  • infoveillance
  • public health
  • public perception
  • sentiment analysis
  • topic modeling


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