Search

Objective: This study aimed to investigate the interest in online and in‐person psychological support services in Italy during the COVID‐19 pandemic, stimulated by the implementation of the government‐issued 'bonus psicologo' for psychotherapy sessions. Methods and Measures: Public interest was measured using data from Google Trends, a resource that offers open access to actual search requests, within the time frame of 2 July 2017 to 13 November 2022. These data were stratified for Italian regions to explore regional differences. We conducted interrupted time series analyses to assess changes in interest before, during and after the lockdown. Results: Results indicated a significant increase in interest in online psychological support during the lockdown, which was sustained over time and was positively correlated with the implementation of the 'bonus psicologo' (p <.001). Interest in in‐person support decreased during the lockdown but began to rise post‐lockdown (p =.028), with no notable impact from the economic incentive. High interest in online support continued in certain regions even 1 year post‐lockdown, warranting further analysis. Conclusion: The study underscores a heightened post‐pandemic interest in psychological support, both online and in person, in comparison with pre‐pandemic levels. Google Trends emerges as a valuable tool for monitoring public interest during specific events, though the findings must be interpreted with care, given the study's time frame and reliance on relative search volume as a measure. Future research is needed to validate these findings and to compare them against epidemiological data. [ABSTRACT FROM AUTHOR]

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.

The purpose of this observational study was to evaluate the potential epidemiological trend of arboviral diseases most reported in Italy by the dedicated national surveillance system (ISS data) compared to searches on the internet, assessing whether a correlation/association between users' searches in Google and Wikipedia and real cases exists. The study considers a time interval from June 2012 to December 2023. We used the following Italian search terms: "Virus Toscana", "Virus del Nilo occidentale" (West Nile Virus in English), "Encefalite trasmessa da zecche" (Tick Borne encephalitis in English), and "Dengue". We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using Pearson's correlation coefficient (r) or Spearman's rank correlation coefficient (rho) as appropriate. All the correlations between the ISS data and Wikipedia or GT exhibited statistical significance. The correlations were strong for Dengue GT and ISS (rho = 0.71) and TBE GT and ISS (rho = 0.71), while the remaining correlations had values of r and rho between 0.32 and 0.67, showing a moderate temporal correlation. The observed correlations and regression models provide a foundation for future research, encouraging a more nuanced exploration of the dynamics between digital information-seeking behavior and disease prevalence. [ABSTRACT FROM AUTHOR]

The purpose of this observational study was to evaluate the potential epidemiological trend of arboviral diseases most reported in Italy by the dedicated national surveillance system (ISS data) compared to searches on the internet, assessing whether a correlation/association between users’ searches in Google and Wikipedia and real cases exists. The study considers a time interval from June 2012 to December 2023. We used the following Italian search terms: “Virus Toscana”, “Virus del Nilo occidentale” (West Nile Virus in English), “Encefalite trasmessa da zecche” (Tick Borne encephalitis in English), and “Dengue”. We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using Pearson’s correlation coefficient (r) or Spearman’s rank correlation coefficient (rho) as appropriate. All the correlations between the ISS data and Wikipedia or GT exhibited statistical significance. The correlations were strong for Dengue GT and ISS (rho = 0.71) and TBE GT and ISS (rho = 0.71), while the remaining correlations had values of r and rho between 0.32 and 0.67, showing a moderate temporal correlation. The observed correlations and regression models provide a foundation for future research, encouraging a more nuanced exploration of the dynamics between digital information-seeking behavior and disease prevalence.

Copyright of Journal der Deutschen Dermatologischen Gesellschaft is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Background and objectives: In recent years, there has been an increase in skin cancer. The aim of this study was therefore to investigate the representation of skin cancer in public awareness worldwide and in Germany, and to determine whether Skin Cancer Awareness Month is represented in the search interests of the Internet‐using population in the same way as Breast Cancer Awareness Month worldwide. Data and methods: In this study, Google Trends data were used to track levels of public awareness for different tumor entities and skin cancer types worldwide and for Germany. Results: The results of this analysis clearly showed a high level of relative public search interest in breast cancer worldwide in the awareness month of October. Worldwide and in Germany, there was a certain increase in search interest and a certain seasonal effect around the May awareness month for skin cancer. For example, the analysis showed a search interest in May and during the summer months in Germany. Conclusions: It is likely that the population, for example in Germany, may benefit further from an even greater emphasis on the topic of skin cancer. [ABSTRACT FROM AUTHOR]

Contagious exanthematous diseases are becoming a major public health problem. The purpose of this study was to evaluate the potential epidemiological trend of four infectious exanthematous diseases in Italy through the searches on the internet. We used the following Italian search term: 'Sesta malattia' (Sixth Disease, in English), 'Eritema Infettivo' (also knows 'Quinta malattia' in Italian; Fifth Disease in English), 'Quarta malattia' (Fourth Disease in English) and 'Scarlattina' (Scarlet fever in English). We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). The study period is between July 2015 and December 2022 The diseases considered have a seasonal trend and the search peaks between GT and Wikipedia overlap. A temporal correlation was observed between GT and Wikipedia search trends. Google Trends Internet search data showed strong correlation with Wikipedia with a rho statistically significant for Fifth disease (rho = 0.78), Fourth disease (rho = 0.76) and Scarlet-fever (rho = 0.77), moderate correlation for Sixth disease (rho = 0.32). Infectious disease searches using Google and Wikipedia can be useful for public health surveillance and help policy makers implement prevention and information programs for the population, in addition to the fact that increases in searches could represent an early warning in the detection of outbreaks. [ABSTRACT FROM AUTHOR]

The Internet is an important gateway for accessing health-related information, and data generated through web queries have been increasingly used as a complementary source for monitoring and forecasting of infectious diseases and they may partially address the issue of underreporting. In this study, we assessed whether tick-borne encephalitis (TBE)-related Internet search volume may be useful as a complementary tool for TBE surveillance in Italy. Monthly Google Trends (GT) data for TBE-related information were extracted for the period between January 2017 and September 2022, corresponding to the available time series of TBE notifications in Italy. Time series modeling was performed by applying seasonal autoregressive integrated moving average (SARIMA) models with or without GT data. The search terms relative to tick bites reflected best the observed temporal distribution of TBE cases, showing a correlation coefficient of 0.81 (95 % CI: 0.71–0.88). Particularly, both the reported number of TBE cases and GT searches occurred mainly during the summer. The peak of disease notifications coincided with that of Google searches in 4 of 6 years. Once calibrated, SARIMA models with or without GT data were applied to a validation set. Retrospective forecast made by the model with GT data was associated with a lower prediction error and accurately predicted the peak timing. By contrast, the traditional SARIMA model underestimated the actual number of TBE notifications by 65 %. Timeliness, easy availability, low cost and transparency make monitoring of the TBE-related Internet search queries a promising addition to the traditional methods of TBE surveillance in Italy.

BackgroundThere is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information.ObjectiveThis paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change.MethodsWe used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database.ResultsStatistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P

Background: The coronavirus disease (COVID-19) pandemic has impacted patients receiving methadone maintenance treatment (MMT) through opioid treatment programs (OTPs), especially because of the unique challenges of the care delivery model. Previously, documentation of patient experiences during emergencies often comes years after the fact, in part because there is a substantial data void in real-time. Methods: We extracted 308 posts that mention COVID-19 keywords on r/methadone, an online community for patients receiving MMT to share information, on Reddit occurring between January 31, 2020 and September 30, 2020. 215 of these posts self-report an impact to their MMT. Using qualitative content analysis, we characterized the impacts described in these posts and identified four emergent themes describing patients' experience of impacts to MMT during COVID-19. Results: The themes included (1) 54.4% of posts reporting impediments to accessing their methadone, (2) 28.4% reporting impediments to accessing physicial OTPs, (3) 19.5% reporting having to self-manage their care, and (4) 4.7% reporting impediments to accessing OTP providers and staff. Conclusions: Patients described unanticipated consequences to one-size-fits-all policies that are unevenly applied resulting in suboptimal dosing, increased perceived risk of acquiring COVID-19 at OTPs, and reduced interaction with OTP providers and staff. While preliminary, these results are formative for follow-up surveillance metrics for patients of OTPs as well as digitally-mediated resource needs for this online community. This study serves as a model of how social media can be employed during and after emergencies to hear the lived experiences of patients for informed emergency preparedness and response.

Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called "monkeypox" but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue. Objective: In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter. Results: By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6%), health policy and health care (8400/43,521, 19.3%), homophobia (10,402/43,521, 23.9%), politics (2611/43,521, 6%), and racism (1784/43,521, 4.1%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making. [ABSTRACT FROM AUTHOR]

Background: Although researchers extensively study the rapid generation and spread of misinformation about the novel coronavirus during the pandemic, numerous other health-related topics are contaminating the internet with misinformation that have not received as much attention. Objective: This study aims to gauge the reach of the most popular medical content on the World Wide Web, extending beyond the confines of the pandemic. We conducted evaluations of subject matter and credibility for the years 2021 and 2022, following the principles of evidence-based medicine with assessments performed by experienced clinicians. Methods: We used 274 keywords to conduct web page searches through the BuzzSumo Enterprise Application. These keywords were chosen based on medical topics derived from surveys administered to medical practitioners. The search parameters were confined to 2 distinct date ranges: (1) January 1, 2021, to December 31, 2021; (2) January 1, 2022, to December 31, 2022. Our searches were specifically limited to web pages in the Polish language and filtered by the specified date ranges. The analysis encompassed 161 web pages retrieved in 2021 and 105 retrieved in 2022. Each web page underwent scrutiny by a seasoned doctor to assess its credibility, aligning with evidence-based medicine standards. Furthermore, we gathered data on social media engagements associated with the web pages, considering platforms such as Facebook, Pinterest, Reddit, and Twitter. Results: In 2022, the prevalence of unreliable information related to COVID-19 saw a noteworthy decline compared to 2021. Specifically, the percentage of noncredible web pages discussing COVID-19 and general vaccinations decreased from 57% (43/76) to 24% (6/25) and 42% (10/25) to 30% (3/10), respectively. However, during the same period, there was a considerable uptick in the dissemination of untrustworthy content on social media pertaining to other medical topics. The percentage of noncredible web pages covering cholesterol, statins, and cardiology rose from 11% (3/28) to 26% (9/35) and from 18% (5/28) to 26% (6/23), respectively. Conclusions: Efforts undertaken during the COVID-19 pandemic to curb the dissemination of misinformation seem to have yielded positive results. Nevertheless, our analysis suggests that these interventions need to be consistently implemented across both established and emerging medical subjects. It appears that as interest in the pandemic waned, other topics gained prominence, essentially "filling the vacuum" and necessitating ongoing measures to address misinformation across a broader spectrum of health-related subjects. [ABSTRACT FROM AUTHOR]

Copyright of Epidemiological Review / Przegląd Epidemiologiczny is the property of National Institute of Public Health - National Institute of Hygiene and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose of Review: The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research. Recent Findings: Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. Summary: User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world. [ABSTRACT FROM AUTHOR]

An infodemic is an information epidemic capable of compromising public health. This manuscript proposes an infoveillance method suitable for listening to web concerns on health to develop adequate infodemiological responses based on the World Health Organization indications. In particular, the case of COVID-19 vaccinations in Italy was investigated. Web interest and concern in COVID-19 vaccines over the past week (January 8–14, 2023) was investigated via the websites Google Trends and Talkwalker by searching for appropriate keywords. Thanks to the analysis of related queries and topics, it was possible to determine and examine the most debated topics relating to specific side effects. Emotional reactions regarding COVID-19 vaccines have been negative in varying percentages between 40 and 70 %, depending on the topic discussed. Feelings of alarm, derision, doubt, and anger were common (about 60 %). The concerns were mainly about the effectiveness against recent COVID-19 variants and alleged side effects such as sudden death, tumors, myocarditis, prion disease, and high ferritin. The most used media among those scrutinized was Twitter (over 90 % of interactions). The male audience participated more and showed more negativity than the female one. The age groups mainly involved were the under-45s. This research discussed the combined use of Google Trends and Talkwalker to conduct rapid infoveillance surveys. The results found showed that the web public has many doubts about COVID-19 vaccines, including the appearance of very rare or unproved side effects. Based on the WHO infodemic management strategy, it is essential that this or similar approaches are adopted by health and government authorities to listen to the community and calibrate appropriate infodemiological responses aimed at preserving public health.

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic’s enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke.

BackgroundReal-time, rapid assessment of barriers to care experienced by patients can be used to inform relevant health care legislation. In recent years, online communities have become a source of support for patients as well as a vehicle for discussion and collaboration among patients, clinicians, advocates, and researchers. The Breast Cancer Social Media (#BCSM) community has hosted weekly Twitter chats since 2011. Topics vary each week, and chats draw a diverse group of participants. Partnering with the #BCSM community, we used Twitter to gather data on barriers to care for patients with metastatic breast cancer and potential policy solutions. Metastatic breast cancer survival rates are low and in large part conditioned by time-sensitive access to care factors that might be improved through policy changes.ObjectiveThis study was part of an assessment of the barriers to care for metastatic breast cancer with the goal of offering policy solutions for the legislative session in California.MethodsWe provided 5 questions for a chat specific to metastatic breast cancer care barriers and potential policy solutions. These were discussed during the course of a #BCSM chat on November 18, 2019. We used Symplur (Symplur LLC) analytics to generate a transcript of tweets and a profile of participants. Responses to the questions are presented in this paper.ResultsThere were 288 tweets from 42 users, generating 2.1 million impressions during the 1-hour chat. Participants included 23 patient advocates (most of whom were patients themselves), 7 doctors, 6 researchers or academics, 3 health care providers (2 nurses, 1 clinical psychologist), and 2 advocacy organizations. Participants noted communication gaps between patient and provider especially as related to the need for individualized medication dosing to minimize side effects and maximize quality of life. Timeliness of insurance company response, for example, to authorize treatments, was also a concern. Chat participants noted that palliative care is not well integrated into metastatic breast cancer care and that insurance company denials of coverage for these services were common. Regarding financial challenges, chat participants mentioned unexpected copays, changes in insurance drug formularies that made it difficult to anticipate drug costs, and limits on the number of physical therapy visits covered by insurance. Last, on the topic of disability benefits, participants expressed frustration about how to access disability benefits. When prompted for input regarding what health system and policy changes are necessary, participants suggested a number of ideas, including expanding the availability of nurse navigation for metastatic breast cancer, developing and offering a guide for the range of treatment and support resources patients with metastatic breast cancer, and improving access to clinical trials.ConclusionsRapid assessments drawing from online community insights may be a critical source of data that can be used to ensure more responsive policy action to improve patient care.

Introduction: College-aged youth are active on social media yet smoking-related social media engagement in these populations has not been thoroughly investigated. We sought to conduct an exploratory infoveillance study focused on geolocated data to characterize smoking-related tweets originating from California 4-year colleges on Twitter. Methods: Tweets from 2015 to 2019 with geospatial coordinates in CA college campuses containing smoking-related keywords were collected from the Twitter API stream and manually annotated for discussions about smoking product type, sentiment, and behavior. Results: Out of all tweets detected with smoking-related behavior, 46.7% related to tobacco use, 50.0% to marijuana, and 7.3% to vaping. Of these tweets, 46.1% reported first-person use or second-hand observation of smoking behavior. Out of 962 tweets with user sentiment, the majority (67.6%) were positive, ranging from 55.0% for California State University, Long Beach to 95.8% for California State University, Los Angeles. Discussion: We detected reporting of first- and second-hand smoking behavior on CA college campuses representing possible violation of campus smoking bans. The majority of tweets expressed positive sentiment about smoking behaviors, though there was appreciable variability between college campuses. This suggests that anti-smoking outreach should be tailored to the unique student populations of these college communities. Conclusion: Among tweets about smoking from California colleges, high levels of positive sentiment suggest that the campus climate may be less receptive to anti-smoking messages or adherence to campus smoking bans. Further research should investigate the degree to which this varies by campuses over time and following implementation of bans including validating using other sources of data.

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns. [ABSTRACT FROM AUTHOR]

BackgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.ObjectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.MethodsWe used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19-related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.ResultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior.ConclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

BackgroundIncreases in electronic nicotine delivery system (ENDS) use among high school students from 2017 to 2019 appear to be associated with the increasing popularity of the ENDS device JUUL.ObjectiveWe employed a content analysis approach in conjunction with natural language processing methods using Twitter data to understand salient themes regarding JUUL use on Twitter, sentiment towards JUUL, and underage JUUL use.MethodsBetween July 2018 and August 2019, 11,556 unique tweets containing a JUUL-related keyword were collected. We manually annotated 4000 tweets for JUUL-related themes of use and sentiment. We used 3 machine learning algorithms to classify positive and negative JUUL sentiments as well as underage JUUL mentions.ResultsOf the annotated tweets, 78.80% (3152/4000) contained a specific mention of JUUL. Only 1.43% (45/3152) of tweets mentioned using JUUL as a method of smoking cessation, and only 6.85% (216/3152) of tweets mentioned the potential health effects of JUUL use. Of the machine learning methods used, the random forest classifier was the best performing algorithm among all 3 classification tasks (ie, positive sentiment, negative sentiment, and underage JUUL mentions).ConclusionsOur findings suggest that a vast majority of Twitter users are not using JUUL to aid in smoking cessation nor do they mention the potential health benefits or detriments of JUUL use. Using machine learning algorithms to identify tweets containing underage JUUL mentions can support the timely surveillance of JUUL habits and opinions, further assisting youth-targeted public health intervention strategies.

BackgroundThe coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries.ObjectiveThe aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach.MethodsTweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics.ResultsA total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (

BackgroundThe coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak.ObjectiveThe aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak.MethodsChinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics.ResultsA total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context.

On 24 February 2022, the invasion of Ukraine by Russian troops began, starting a dramatic conflict. As in all modern conflicts, the battlefield is both real and virtual. Social networks have had peaks in use and many scholars have seen a strong risk of disinformation. In this study, through an unsupervised topic tracking system implemented with Natural Language Processing and graph-based techniques framed within a biological metaphor, the Italian social context is analyzed, in particular, by processing data from Twitter (texts and metadata) captured during the first month of the war. The system, improved if compared to previous versions, has proved to be effective in highlighting the emerging topics, all the main events and any links between them. [ABSTRACT FROM AUTHOR]

Intensified preventive measures during the COVID-19 pandemic, such as lockdown and social distancing, heavily increased the perception of social isolation (i.e., a discrepancy between one's social needs and the provisions of the social environment) among young adults. Social isolation is closely associated with situational loneliness (i.e., loneliness emerging from environmental change), a risk factor for depressive symptoms. Prior research suggested vulnerable young adults are likely to seek support from an online social platform such as Reddit, a perceived comfortable environment for lonely individuals to seek mental health help through anonymous communication with a broad social network. Therefore, this study aims to identify and analyze depression-related dialogues on loneliness subreddits during the COVID-19 outbreak, with the implications on depression-related infoveillance during the pandemic. Our study utilized logistic regression and topic modeling to classify and examine depression-related discussions on loneliness subreddits before and during the pandemic. Our results showed significant increases in the volume of depression-related discussions (i.e., topics related to mental health, social interaction, family, and emotion) where challenges were reported during the pandemic. We also found a switch in dominant topics emerging from depression-related discussions on loneliness subreddits, from dating (prepandemic) to online interaction and community (pandemic), suggesting the increased expressions or need of online social support during the pandemic. The current findings suggest the potential of social media to serve as a window for monitoring public mental health. Our future study will clinically validate the current approach, which has implications for designing a surveillance system during the crisis. [ABSTRACT FROM AUTHOR]

In 2019, the U.S. Food and Drug Administration (FDA) approved emtricitabine and tenofovir alafenamide (Descovy) as another option for HIV pre-exposure prophylaxis (PrEP) prevention for high-risk adults and adolescents. With the introduction of this new PrEP, millions of current users on emtricitabine and tenofovir disoproxil fumarate (Truvada), another PrEP medication currently used to prevent HIV transmission, have options of whether to continue their current treatment regime or transition to new treatment options. The objective of this study was to conduct a descriptive analysis to characterize user-generated social media conversations on Reddit associated with FDA-approved PrEP prevention treatment options. Key themes identified were associated with perceptions, knowledge, and attitudes associated with the transition of use of different PrEP medications. Data were collected retrospectively and prospectively from the Reddit platform for posts with keywords filtered for HIV, PrEP, and FDA-approved PrEP prevention treatment from October 2020 to December 2020. We chose the Reddit platform based on prior studies that have identified PrEP user conversations and insights on access challenges for specific AIDS communities, such as gays and men who have sex with men (MSM). Reddit posts were then manually annotated using an inductive content coding approach for key themes regarding the transition of use and other emergent themes from user-generated content. Formal coding of text data was conducted with refined codes, and sub-codes created. A total of 3,120 posts were analyzed from Reddit resulting in 315 posts that were coded for PrEP and 105 posts (33.33%) specific to user discussions regarding the transition of PrEP prevention. Overall, users expressed interest in drug switching to Descovy, particularly in the context of poorer adherence or concerns about existing side effects associated with Truvada. Other major themes included discussions about the cost of Descovy, apprehension about side effects in comparison to Truvada, insurance coverage changes, and discussions about the donation of Truvada to other users after transitioning. Among these discussions, topics related to sexual minorities, including MSM, reported concerns when considering a switch in their HIV prevention regime. Understanding the changing public perception associated with the introduction of new HIV prevention is important in the context of market access, patient safety, pharmacovigilance, and health equity, particularly among high-risk populations such as MSM. Results support the use of social media from a digital pharmacovigilance perspective to better understand emerging HIV prevention, treatment, and adherence challenges experienced by patients.

Study Objective: Web-based big data analytics provides a great opportunity to measure public interest in cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). This study aimed to examine associations of online interest in CPR and CA with epidemiological characteristics of out-of-hospital CA (OHCA) and national socioeconomic indicators in a set of European countries. Methods: Country-level online search popularity data for CPR and CA topics measured in relative search volume (RSV) with Google Trends (GT), published OHCA epidemiological indicators, and World Bank's socioeconomic statistics of 28 European countries for the year 2017 were analyzed for correlation using Spearman's rank correlation coefficient (r S ). Results: Whereas OHCA incidence, bystander CPR rate, and hospital survival did not correlate with RSV for CPR or CA, the rate of return of spontaneous circulation (ROSC) demonstrated a positive correlation with RSV for CPR (r S = 0.388; P =.042). Further, RSV for CPR positively correlated with countries' gross domestic product and health expenditure (r S = 0.939 and 0.566; P ≤.002) and negatively correlated with mortality caused by road traffic injury (r S = –0.412; P =.029). Conclusion: For the sample of European countries, public interest in CPR or CA showed no relationship with real bystander CPR rates and therefore could not be recommended as a proxy of community readiness to attempt resuscitation. The association of RSV for CPR with the rate of ROSC and countries' socioeconomic characteristics suggests it could be used for identifying geographies with poor performance of prehospital systems in terms of managing CA, in particular where effective epidemiological surveillance for CA may be unavailable. [ABSTRACT FROM AUTHOR]

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context. [ABSTRACT FROM AUTHOR]

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Big data originating from user interactions on social media play an essential role in infodemiology and infoveillance outcomes, supporting the planning and implementation of public health actions. Notably, the extrapolation of these data requires an awareness of different ethical elements. Previous studies have investigated and discussed the adoption of conventional ethical approaches in the contemporary public health digital surveillance space. However, there is a lack of specific ethical guidelines to orient infodemiology and infoveillance studies concerning infodemic on social media, making it challenging to design digital strategies to combat this phenomenon. Hence, it is necessary to explore if traditional ethical pillars can support digital purposes or whether new ones must be proposed since we are confronted with a complex online misinformation scenario. Therefore, this perspective provides an overview of the current scenario of ethics-related issues of infodemiology and infoveillance on social media for infodemic studies.

Google Trends (GT) is a useful real-time surveillance tool for epidemic outbreaks such as monkeypox (Mpox). GT provides hour-by-hour (real-time) data for the last seven days of Google searches. Non-real-time data are a random sample that encompasses search trends from 2004 and up to 72 h. Google Health Trends (GHT) API extracts daily raw search probabilities relative to the time period and size of the underlying population. However, little is known about the utility of GT real-time surveillance and GHT API following the public health announcements. Thus, this study aimed to analyzed Mpox GT real-time, non-real-time, and GHT API data 72 h before and after the WHO declared Mpox a public health emergency of international concern (PHEIC) in the top five Mpox-affected countries. Joinpoint regression was used to measure hourly percentage changes (HPC) in search volume. The WHO PHEIC statement on Mpox generated 18,225.6 per 10 million Google searches in the U.S. and Germany (946.8), and in 0–4 h, the HPC increased by an average of 103% (95% CI: 37.4–200.0). This study showed the benefits of real-time surveillance and the GHT API for monitoring online demand for information on emerging infectious diseases such as Mpox. [ABSTRACT FROM AUTHOR]

Background: The internet is a primary source of health information for patients, supplementing physician care. Google Trends (GT), a popular tool, allows the exploration of public interest in health-related phenomena. Despite the growing volume of GT studies, none have focused explicitly on oncology, creating a need for a systematic review to bridge this gap. Objective: We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases. Methods: We included all studies that used GT to analyze Google searches related to malignancies. We excluded studies written in languages other than English. The search was performed using the PubMed engine on August 1, 2022. We used the following search input: "Google trends" AND ("oncology" OR "cancer" or "malignancy" OR "tumor" OR "lymphoma" OR "multiple myeloma" OR "leukemia"). We analyzed sources of bias that included using search terms instead of topics, lack of confrontation of GT statistics with real-world data, and absence of sensitivity analysis. We performed descriptive statistics. Results: A total of 85 articles were included. The first study using GT for oncology research was published in 2013, and since then, the number of publications has increased annually. The studies were categorized as follows: 22% (19/85) were related to prophylaxis, 20% (17/85) pertained to awareness events, 11% (9/85) were celebrity-related, 13% (11/85) were related to COVID-19, and 47% (40/85) fell into other categories. The most frequently analyzed cancers were breast (n=28), prostate (n=26), lung (n=18), and colorectal cancers (n=18). We discovered that of the 85 studies, 17 (20%) acknowledged using GT topics instead of search terms, 79 (93%) disclosed all search input details necessary for replicating their results, and 34 (40%) compared GT statistics with real-world data. The most prevalent methods for analyzing the GT data were correlation analysis (55/85, 65%) and peak analysis (43/85, 51%). The authors of only 11% (9/85) of the studies performed a sensitivity analysis. Conclusions: The number of studies related to oncology using GT data has increased annually. The studies included in this systematic review demonstrate a variety of concerning topics, search strategies, and statistical methodologies. The most frequently analyzed cancers were breast, prostate, lung, colorectal, skin, and cervical cancers, potentially reflecting their prevalence in the population or public interest. Although most researchers provided reproducible search inputs, only one-fifth used GT topics instead of search terms, and many studies lacked a sensitivity analysis. Scientists using GT for medical research should ensure the quality of studies by providing a transparent search strategy to reproduce results, preferring to use topics over search terms, and performing robust statistical calculations coupled with sensitivity analysis. [ABSTRACT FROM AUTHOR]

Background: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis–driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. Objective: This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time. Methods: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. Results: We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. Conclusions: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population. [ABSTRACT FROM AUTHOR]

Background: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. Objective: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. Methods: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. Results: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave–related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. Conclusions: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics. [ABSTRACT FROM AUTHOR]

Background: Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people's needs during a global health emergency. Objective: This study examined the COVID-19 pandemic–related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods: We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world's largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19–related chats across countries. Results: Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: "Questions on COVID-19 asked to the chatbot" (30.6%), "Preventive behaviors" (25.3%), "Outbreak of COVID-19" (16.4%), "Physical and psychological impact of COVID-19" (16.0%), and "People and life in the pandemic" (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions: Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people's informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy. [ABSTRACT FROM AUTHOR]

Background: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. Objective: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. Methods: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. Results: There were fewer neutral mask-related tweets in 2020 (β=–3.94 percentage points, 95% CI –4.68 to –3.21; P <.001) and 2021 (β=–8.74, 95% CI –9.31 to –8.17; P <.001). Following the April 3 recommendation (β=.51, 95% CI.43-.59; P <.001) and May 13 relaxation (β=3.43, 95% CI 1.61-5.26; P <.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (β=–.004, 95% CI –.004 to –.003; P <.001) and May 13 (β=–.001, 95% CI –.002 to 0; P =.008). Conclusions: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly. [ABSTRACT FROM AUTHOR]

Background: Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delay in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions. [ABSTRACT FROM AUTHOR]

The complex interplay between sleep-related information-both accurate and misleading-and its impact on clinical public health is an emerging area of concern. Lack of awareness of the importance of sleep, and inadequate information related to sleep, combined with misinformation about sleep, disseminated through social media, nonexpert advice, commercial interests, and other sources, can distort individuals' understanding of healthy sleep practices. Such misinformation can lead to the adoption of unhealthy sleep behaviors, reducing sleep quality and exacerbating sleep disorders. Simultaneously, poor sleep itself impairs critical cognitive functions, such as memory consolidation, emotional regulation, and decision-making. These impairments can heighten individuals' vulnerability to misinformation, creating a vicious cycle that further entrenches poor sleep habits and unhealthy behaviors. Sleep deprivation is known to reduce the ability to critically evaluate information, increase suggestibility, and enhance emotional reactivity, making individuals more prone to accepting persuasive but inaccurate information. This cycle of misinformation and poor sleep creates a clinical public health issue that goes beyond individual well-being, influencing occupational performance, societal productivity, and even broader clinical public health decision-making. The effects are felt across various sectors, from health care systems burdened by sleep-related issues to workplaces impacted by decreased productivity due to sleep deficiencies. The need for comprehensive clinical public health initiatives to combat this cycle is critical. These efforts must promote sleep literacy, increase awareness of sleep's role in cognitive resilience, and correct widespread sleep myths. Digital tools and technologies, such as sleep-tracking devices and artificial intelligence-powered apps, can play a role in educating the public and enhancing the accessibility of accurate, evidence-based sleep information. However, these tools must be carefully designed to avoid the spread of misinformation through algorithmic biases. Furthermore, research into the cognitive impacts of sleep deprivation should be leveraged to develop strategies that enhance societal resilience against misinformation. Sleep infodemiology and infoveillance, which involve tracking and analyzing the distribution of sleep-related information across digital platforms, offer valuable methodologies for identifying and addressing the spread of misinformation in real time. Addressing this issue requires a multidisciplinary approach, involving collaboration between sleep scientists, health care providers, educators, policy makers, and digital platform regulators. By promoting healthy sleep practices and debunking myths, it is possible to disrupt the feedback loop between poor sleep and misinformation, leading to improved individual health, better decision-making, and stronger societal outcomes., (©Nicola Luigi Bragazzi, Sergio Garbarino. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 13.12.2024.)

Background: In response to the well-documented fragmentation within its mental health system, Australia has witnessed recently rapid expansion in the availability of digital mental health care navigation tools. These tools focus on assisting consumers to identify and access appropriate mental health care services, the proliferation of such varied web-based resources risks perpetuating further fragmentation and confusion for consumers. There is a pressing need to systematically assess the characteristics, comprehensiveness, and validity of these navigation tools, especially as demand for digital resources continues to escalate., Objective: This study aims to identify and describe the current landscape of Australian digital mental health care navigation tools, with a focus on assessing their comprehensiveness, identifying potential gaps, and the extent to which they meet the needs of various stakeholders., Methods: A comprehensive infoveillance approach was used to identify Australian digital mental health care navigation tools. This process involved a systematic web-based search complemented by consultations with subject matter experts. Identified navigation tools were independently screened by 2 authors, while data extraction was conducted by 3 authors. Extracted data were mapped to key domains and subdomains relevant to navigation tools., Results: From just a handful in 2020, by February 2024 this study identified 102 mental health care navigation tools across Australia. Primary Health Networks (n=37) and state or territory governments (n=21) were the predominant developers of these tools. While the majority of navigation tools were primarily designed for consumer use, many also included resources for health professionals and caregivers. Notably, no navigation tools were specifically designed for mental health care planners. Nearly all tools (except one) featured directories of mental health care services, although their functionalities varied: 27% (n=27) provided referral information, 20% (n=21) offered geolocated service maps, 12% (n=12) included diagnostic screening capabilities, and 7% (n=7) delineated care pathways., Conclusions: The variability of navigation tools designed to facilitate consumer access to mental health services could paradoxically contribute to further confusion. Despite the significant expansion of digital navigation tools in recent years, substantial gaps and challenges remain. These include inconsistencies in tool formats, resulting in variable information quality and validity; a lack of regularly updated service information, including wait times and availability for new clients; insufficient details on program exclusion criteria; and limited accessibility and user-friendliness. Moreover, the inclusion of self-assessment screening tools is infrequent, further limiting the utility of these resources. To address these limitations, we propose the development of a national directory of mental health navigation tools as a centralized resource, alongside a system to guide users toward the most appropriate tool for their individual needs. Addressing these issues will enhance consumer confidence and contribute to the overall accessibility, reliability, and utility of digital navigation tools in Australia's mental health system., (© Cindy E Woods, Mary-Anne Furst, Manoj Dissanayake, Jane Koerner, Carlota de Miquel, Sue Lukersmith, Sebastian Rosenberg, Luis Salvador-Carulla. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Background: Digitally-mediated peer support may improve opioid use disorder (OUD) recovery. Our objective was to examine the types and sources of stigma that people seek support for in online OUD recovery communities (subreddits) on Reddit. Methods: We extracted all posts containing stigma keywords from three subreddits as well as a random sample that do not contain stigma keywords. We conducted deductive content analysis to confirm that the post self-described an experience of stigma and identify the type (condition, intervention) and source (provider-based, public, self, structural) of stigma. Results: Two-hundred and fifty-nine posts self-reported a stigmatizing experience. The majority of posts described an intervention stigma associated with medications for OUD. Posts discussing intervention stigma acknowledged the role of stigma in their treatment decision-making and quality of their treatment program. The most frequent sources of stigma were the public (including family members), provider-based (healthcare and pharmacy workers), structural (workplace, law enforcement, child protective services, and abstinence-based self-help groups), and self. No posts mentioned courtesy stigma. Posts sought assistance in navigating their experiences and participating in advocacy to counter stigmatized narratives. Conclusions: Our study indicates that people in online communities seek support to disclose and manage experiences of stigma on Reddit in similar ways to people in offline communities with the noted exception of an absence of discussions of courtesy stigma. Since each subreddit is a microcosm of varying needs, we suggest areas of future work for collaborative resources developed between stakeholders of these subreddits and public health that work within the preexisting Reddit social norms. [ABSTRACT FROM AUTHOR]

The infodemic is an important component of the cyber-risk in regard to the poor and uncontrolled dissemination of information related to the COVID-19 pandemic. The purpose of this study was to perform a narrative review based on three points of view to allow for an overall picture of this issue. The points of view focused on: (a) the volume of use of social media (a key element of the infodemic) and the position of international health domain bodies; (b) the evolution of scientific production in the life sciences; (c) emerging issues. The research methodology was based on Google and PubMed searches and a qualification process based on a standard checklist and an evaluation of eligibility based on parameters with five score levels applied by two experts (plus one in case of discrepancy). The three points of view stressed the key role of social media as a dissemination tool of the infodemic among citizens. The impact on citizens depends on various social factors and involves indirect (e.g., vaccine avoidance) and direct risks such as mental problems and the risk of suicide. The widespread diffusion of social media, conveyed by mobile technologies, also suggests their use as countermeasures, calibrated based on citizens' level of both technological and health literacy. Effective and promising countermeasures in this direction are based both on initiatives of contact by apps or SMS and the collection of data based on surveys and finalized to the particular intervention. The review also suggests as further areas of in-depth research: (a) to combat high-level infodemic produced by scientific publications that are not yet official (preprint) or that have undergone peer review with bias/distortion; (b) focusing on the impact of the infodemic considering its spread in different languages. [ABSTRACT FROM AUTHOR]

Background: Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, individuals with allergic rhinitis typically self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it represents a major social problem because it reduces people’s quality of life, making it essential to understand its prevalence and people’s motives for self-medication behavior. Objective: This study aims to explore the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan. Methods: We collected tweets over 4 years (from 2017 to 2020) that included keywords corresponding to the main nasal symptoms of allergic rhinitis: “sneezing,” “runny nose,” and “stuffy nose.” We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week. Results: The results showed a much higher correlation (r=0.8432) between the time series data on the number of tweets mentioning “stuffy nose” and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (r=0.9317) between the seasonal components of these time series data. Conclusions: We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships can help us understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, which would be useful as a marketing indicator to reduce the number of out-of-stocks in stores, provide (sell) rhinitis medicines to consumers in a stable manner, and reduce the loss of sales opportunities. In the future, in-depth investigations are required to estimate sales volume using social media data, and future research could investigate other diseases and countries. [ABSTRACT FROM AUTHOR]

Background: Climate change and related environmental events represent major global challenges and are often accompanied by the spread of misinformation on social media. According to previous reviews, the dissemination of this misinformation on various social media platforms requires deeper exploration. Moreover, the findings reported applied mainly to the context of the United States, limiting the possibility of extending the results to other settings., Objective: This study aims to assess the current state of knowledge about misinformation concerning climate change and related environmental events that are circulating on social media. More specifically, we will explore past and current themes, actors, and sources, and the dissemination of this misinformation within the Canadian context., Methods: This scoping review protocol follows the methodological approach developed by Arksey and O'Malley and advanced by Levac, complemented by the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and the best practice guidance for the development of scoping review protocols. Following the identification of the research questions and assisted by a specialized librarian, we developed search strategies for selected bibliographic databases (MEDLINE, Embase, Web of Science, and GreenFILE) and for gray literature (Google and pertinent databases) searches. Bibliographic and gray literature will be searched to identify relevant publications. In total, 2 members of our team will use the review software Covidence (Veritas Health Innovation) to independently select publications to include in the review. Publications specifically addressing our research questions, peer-reviewed, evidence-based, and published from January 1, 2000, in the full-text version in English or French will be included. Data will be extracted from the included publications to chart, among other items, the years of publication, geographic areas, themes, actors, and sources of the climate change-related misinformation and conclusions reported. Our team will then synthesize the extracted data to articulate the current state of knowledge relating to our research inquiries., Results: The research questions were identified in January 2024. The search strategies were developed from January to March 2024 for MEDLINE, Embase, and Web of Science and in July 2024 for GreenFILE and gray literature. MEDLINE, Embase, and Web of Science searches were launched on March 26, 2024. The first of 2 rounds of selection of publications identified through these databases was achieved in April 2024., Conclusions: This protocol will enable us to identify the evolution of themes, actors, and sources of misinformation regarding climate change and related environmental events on social media, including the latest platforms, and to potentially identify a context particular to Canada. As misinformation is known to undermine actions and public support in the fight against climate change, we intend to facilitate the targeting of efforts to combat misinformation related to climate change in an up-to-date and contextualized manner., International Registered Report Identifier (irrid): DERR1-10.2196/59345., (©Maryline Vivion, Valérie Trottier, Ève Bouhêlier, Isabelle Goupil-Sormany, Thierno Diallo. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 31.10.2024.)

Background: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization's (WHO) infodemiology research agenda, which aims to propose new methods for social listening., Objective: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time., Methods: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the "Gossiping" subforum were scraped from January 2021 to December 2022 using the keyword "vaccine." A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users-or key nodes-in the network, the activity of high-diversity and hardliner nodes was analyzed., Results: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a "healthy community." Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting., Conclusions: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of "influence." To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda., (©Jason Dean-Chen Yin. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 09.08.2024.)

Background: Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media--based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials. Objective: This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer). Methods: The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. Results: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. Conclusions: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. [ABSTRACT FROM AUTHOR]