Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data.

BACKGROUND: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. OBJECTIVE: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. METHODS: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. RESULTS: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. CONCLUSIONS: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset.

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, "government and politics" and "conspiracy theories," and decreased for "developmental disabilities." Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.

Insights into COVID-19: Mining Physicians' Opinions on Social Media

BACKGROUND: The coronavirus pandemic outbreak is considered the most daunting public health challenge in decades. With no effective treatments and the time needed to develop a vaccine, alternatives approaches are followed to control such pandemic. OBJECTIVE: To identify topics, opinions, and recommendations discussed by medical professionals about the coronavirus pandemic outbreak on social medial platform, Twitter. METHODS: Using a mixed-method approach blending the capabilities of social media analytics and qualitative analysis, we analyzed coronavirus related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data in order to identify relevant tweets and uncover important concepts about the phenomenon using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. RESULTS: Data was collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely, actions and recommendations, fighting misinformation, information and knowledge, healthcare system, symptoms and illness, immunity, testing, and infection and transmission. Tweets were mainly focused on actions and recommendations (28%) needed to control the pandemic. Many tweets warned about misleading information (20%) which could lead to more cases infected with the virus. Other tweets discussed general Knowledge and information (9%) about the virus as well as concerns about the healthcare systems and workers (9%). The remaining tweets discussed information about symptoms associated with COVID-19 (8%), immunity (7%), testing (6%), and virus infection and transmission (5%). CONCLUSIONS: Findings indicate that Twitter and social media platforms could help identify important and useful knowledge shared by medical professionals during the pandemic outbreak.