Publication date: Dec 01, 2024
Background Understanding the attitudes and perceptions of the general population is necessary for organizing health promotion initiatives. During outbreaks, social media has a significant impact on creating social perceptions. This study aims to identify and examine the emotions expressed and topics of discussion among Indian citizens related to COVID-19 third wave, from the messages posted on Twitter using text mining techniques. Methods Twitter messages (tweets) were downloaded using Twitter API from June 1, 2021 to July 10, 2021. After pre-processing the downloaded messages, 8933 unique tweets from various individuals were taken into account for the analysis. To identify and extract emotions expressed by the people in the Twitter texts, the text mining and sentiment analysis package “syuzhet” in R software was used. In order to identify the concerns and themes of discussion by the public during the pandemic, topic analysis was done using the Latent Dirichlet Allocation (LDA) technique. To understand and measure the tweets’ reachability in relation to the themes and emotions conveyed, an engagement metrics analysis was also conducted. Results According to the emotional analysis performed on Twitter messages about the COVID-19 third wave, anticipation was exhibited maximum in 4180 tweets, followed by fear in 4070, and trust in 4001 tweets. Results of topic modeling revealed that there were widespread discussions and concerns about preventative measures to deal with the COVID-19 third wave. Engagement metrics verified that the greatest number of individuals liked and retweeted tweets expressing disgust. Maximum people favorited tweets with information on preventive measures for COVID-19, and a large number of individuals re-shared messages comparing various aspects of different COVID-19 waves. Conclusion Data from social media platforms can be used to comprehend public opinions and emotions during pandemics and emergencies. This can assist public health stakeholders in managing pandemic circumstances by planning effective health communication strategies that quickly reach a larger audience. The study’s findings will assist stakeholders and public health experts in effectively using social media to communicate information about COVID-19 that combat people’s negative emotions and concerns. Future research can use a similar approach to comprehend people’s perspectives and concerns during outbreaks and emergency circumstances.
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Concepts | Keywords |
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June | covid-19 outbreak |
Mining | emotional analysis |
Pandemic | text mining |
topic modelling | |
Semantics
Type | Source | Name |
---|---|---|
disease | MESH | COVID-19 |
drug | DRUGBANK | Alpha-1-proteinase inhibitor |
disease | MESH | emergencies |
pathway | REACTOME | Reproduction |
disease | MESH | infection |
disease | IDO | intervention |
disease | IDO | country |
disease | MESH | shock |
disease | MESH | death |
disease | MESH | fungus infection |
drug | DRUGBANK | Oxygen |
drug | DRUGBANK | Nonoxynol-9 |
disease | MESH | coronavirus infection |
disease | MESH | panic |
drug | DRUGBANK | (S)-Des-Me-Ampa |
drug | DRUGBANK | Carboxyamidotriazole |