An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak
BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronav...
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| Vydáno v: | Open forum infectious diseases Ročník 7; číslo 7; s. ofaa258 |
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01.07.2020
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| ISSN: | 2328-8957, 2328-8957 |
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| Abstract | BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.MethodsWe extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.ResultsWe evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.ConclusionsTwitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.Twitter can be used to identify the sentiment, emotion, and prominent topics discussed among the public during pandemics, allowing for large-scale, public health interventions with direct and targeted messaging. |
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| AbstractList | Twitter can be used to identify the sentiment, emotion, and prominent topics discussed among the public during pandemics, allowing for large-scale, public health interventions with direct and targeted messaging. Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.BACKGROUNDTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.METHODSWe extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.RESULTSWe evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.CONCLUSIONSTwitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion. BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.MethodsWe extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.ResultsWe evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.ConclusionsTwitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion. BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.MethodsWe extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.ResultsWe evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.ConclusionsTwitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.Twitter can be used to identify the sentiment, emotion, and prominent topics discussed among the public during pandemics, allowing for large-scale, public health interventions with direct and targeted messaging. Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion. |
| Author | Saleh, Sameh N Sumarsono, Andrew Medford, Richard J Lehmann, Christoph U Perl, Trish M |
| AuthorAffiliation | 2 University of Texas Southwestern Medical Center, Department of Internal Medicine , Dallas, Texas, USA 3 University of Texas Southwestern Medical Center, Clinical Informatics Center , Dallas, Texas, USA 1 University of Texas Southwestern Medical Center, Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine , Dallas, Texas, USA |
| AuthorAffiliation_xml | – name: 1 University of Texas Southwestern Medical Center, Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine , Dallas, Texas, USA – name: 3 University of Texas Southwestern Medical Center, Clinical Informatics Center , Dallas, Texas, USA – name: 2 University of Texas Southwestern Medical Center, Department of Internal Medicine , Dallas, Texas, USA |
| Author_xml | – sequence: 1 givenname: Richard J orcidid: 0000-0001-9814-8043 surname: Medford fullname: Medford, Richard J email: richard.medford@utsouthwestern.edu organization: University of Texas Southwestern Medical Center, Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, Dallas, Texas, USA – sequence: 2 givenname: Sameh N surname: Saleh fullname: Saleh, Sameh N organization: University of Texas Southwestern Medical Center, Department of Internal Medicine, Dallas, Texas, USA – sequence: 3 givenname: Andrew surname: Sumarsono fullname: Sumarsono, Andrew organization: University of Texas Southwestern Medical Center, Department of Internal Medicine, Dallas, Texas, USA – sequence: 4 givenname: Trish M orcidid: 0000-0001-9539-2358 surname: Perl fullname: Perl, Trish M organization: University of Texas Southwestern Medical Center, Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, Dallas, Texas, USA – sequence: 5 givenname: Christoph U surname: Lehmann fullname: Lehmann, Christoph U organization: University of Texas Southwestern Medical Center, Clinical Informatics Center, Dallas, Texas, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33117854$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. 2020 The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control... Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and... Twitter can be used to identify the sentiment, emotion, and prominent topics discussed among the public during pandemics, allowing for large-scale, public... |
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| SubjectTerms | Coronaviruses COVID-19 Disease prevention Disease transmission Epidemics Major Social networks |
| Title | An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak |
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