Despite challenges related to the data quality, representativeness, and accuracy of artificial intelligence-driven tools, commercially available social listening platforms have many of the attributes needed to be used for digital public health surveillance of human papillomavirus vaccination misinformation in the online ecosystem.
Background: Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination.
Objective: This study aims to assess the associations between social media use patterns; demographics; and knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines, due to growing and evolving social media use.
Methods: Quota-sampled data were collected through a web-based survey of US adults through the Qualtrics platform, from March 15, 2022, to March 23, 2022, to assess covariates (eg, demographics, vaccination, and political affiliation), frequency of social media use, social media sources of COVID-19 information, as well as knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines. Three linear regression models were used for data analysis.
Results: A total of 1043 participants responded to the survey, with an average age of 45.3 years, among which 49.61% (n=515) of participants were men, 66.79% (n=696) were White, 11.61% (n=121) were Black or African American, 13.15% (n=137) were Hispanic or Latino, 37.71% (n=382) were Democrat, 30.21% (n=306) were Republican, and 25% (n=260) were not vaccinated. After controlling for covariates, users of TikTok (β=-.29, 95% CI -0.58 to -0.004; P=.047) were associated with lower knowledge of COVID-19 guidelines, users of Instagram (β=-.40, 95% CI -0.68 to -0.12; P=.005) and Twitter (β=-.33, 95% CI -0.58 to -0.08; P=.01) were associated with perceiving guidelines as strict, and users of Facebook (β=-.23, 95% CI -0.42 to -0.043; P=.02) and TikTok (β=-.25, 95% CI -0.5 to -0.009; P=.04) were associated with lower adherence to the guidelines (R2 0.06-0.23).
Conclusions: These results allude to the complex interactions between online and physical environments. Future interventions should be tailored to subpopulations based on their demographics and social media site use. Efforts to mitigate misinformation and implement digital public health policy must account for the impact of the digital landscape on knowledge, perceptions, and level of adherence toward prevention guidelines for effective pandemic control.
Background: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan's early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions.
Objective: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period.
Methods: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords "corona," "COVID-19," and "new pneumonia" from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups.
Results: The analysis revealed a mix of positive and negative sentiments, with "joy" significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as "worry" and "disgust," were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group.
Conclusions: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.
Background: Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect.
Objective: This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data.
Methods: COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS.
Results: The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey.
Conclusions: These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies.
Background: The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.
Objective: This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.
Methods: Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.
Results: Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.
Conclusions: Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.
Background: TikTok is a popular social media platform that allows users to create and share content through short videos. It has become a place for everyday users, especially Generation Z users, to share experiences about their reproductive health. Owing to its growing popularity and easy accessibility, TikTok can help raise awareness for reproductive health issues as well as help destigmatize these conversations.
Objective: We aimed to identify and understand the visual, audio, and written components of content that TikTok users create about their reproductive health experiences.
Methods: A sampling framework was implemented to narrow down the analytic data set. The top 6 videos from each targeted hashtag (eg, #BirthControl, #MyBodyMyChoice, and #LoveYourself) were extracted biweekly for 16 weeks (July-November 2020). During data collection, we noted video characteristics such as captioning, music, likes, and cited sources. Qualitative content analysis was performed on the extracted videos.
Results: The top videos in each hashtag were consistent over time; for example, only 11 videos appeared in the top 6 category for #BirthControl throughout the data collection. Most videos fell into 2 primary categories: personal experiences and informational content. Among the personal experiences, people shared stories (eg, intrauterine device removal experiences), crafts (eg, painting their pill case), or humor (eg, celebrations of the arrival of their period). Dancing and demonstrations were commonly used in informational content.
Conclusions: TikTok is used to share messages on myriad reproductive health topics. Understanding users' exposure provides important insights into their beliefs and knowledge of sexual and reproductive health. The study findings can be used to generate valuable information for teenagers and young adults, their health care providers, and their communities. Producing health messages that are both meaningful and accessible will contribute to the cocreation of critical health information for professional and personal use.
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