Abstract
The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions.
Highlights
The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches
To gather relevant tweets in the selected period, we performed a focused crawling by employing both the hashtag #coronavirus and the keyword “coronavirus.” No additional hashtags or keywords were selected since, in the initial phase, “coronavirus” was the most frequently used word to discuss the topic, and the term COVID-19 was introduced at a later time
The sentiment about COVID-19 evaluated by means of Valence Aware Dictionary for sEntiment Reasoning (VADER) was on average negative according to the estimated polarity compound scores and relevant 95% Confidence Intervals (Figure 1)
Summary
The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. We aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Core discussions were explored measuring tweets’ sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports via social media-based preventive interventions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
