JMIR infodemiology最新文献

Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals' self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan.

Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei ("due to COVID-19," "because of COVID-19," or "considering COVID-19"), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words.

Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education.

Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens' perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making.

Background: Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified.

Objective: We aim to develop a scale to assess ethical issues on medical social media (SoMe) and use it to determine the prevalence of these issues among posts with 3 different hashtags: #MedTwitter, #IRad, and #CardioTwitter.

Methods: A scale was developed based on previous descriptions of professionalism and validated via semistructured cognitive interviewing with a sample of 11 clinicians and trainees, interrater agreement, and correlation of 100 posts. The final scale assessed social media posts in 6 domains. This was used to analyze 1500 Twitter posts, 500 each from the 3 hashtags. Analysis of posts was limited to original Twitter posts in English made by health care professionals in North America. The prevalence of potential issues was determined using descriptive statistics and compared across hashtags using the Fisher exact and χ² tests with Yates correction.

Results: The final scale was considered reflective of potential ethical issues of SoMe by participants. There was good interrater agreement (Cohen κ=0.620, P<.01) and moderate to strong positive interrater correlation (=0.602, P<.001). The 6 scale domains showed minimal to no interrelation (Cronbach α=0.206). Ethical concerns across all hashtags had a prevalence of 1.5% or less except the conflict of interest concerns on #IRad, which had a prevalence of 3.6% (n=18). Compared to #MedTwitter, posts with specialty-specific hashtags had more patient privacy and conflict of interest concerns.

Conclusions: The SoMe professionalism scale we developed reliably reflects potential ethical issues. Ethical issues on SoMe are rare but important and vary in prevalence across medical communities.

Background: The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion.

Objective: This study probes into Macao's COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media.

Methods: We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities.

Results: "Vaccine importance" and "COVID-19 risk" were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize "COVID-19 risk" and "vaccine effectiveness," while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions.

Conclusions: This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises.

[This corrects the article DOI: 10.2196/37881.].

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.