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The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources. [ABSTRACT FROM AUTHOR]

Background: Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than 90 % of all vector-borne diseases in North America. Objective: In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US. Methods: The study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied. Results: Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of 90 % . There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time. Conclusions: We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets. [ABSTRACT FROM AUTHOR]

Background: The state of evidence-based transgender healthcare in the United States has been put at risk by the spread of misinformation harmful to transgender people. Health science librarians can alleviate the spread of misinformation by identifying and analyzing its flow through systems that affect access to healthcare. Discussion: The author developed the theory of the Misinformation - Legislation Pipeline by studying the flow of antitransgender misinformation from online echo chambers through a peer-reviewed article and into policy enacted to ban medical treatments for transgender people in the state of Florida. The analysis is precluded with a literature review of currently accepted best practices in transgender healthcare, after which, the author analyzes the key report leveraged by Florida's Department of Health in its ban. A critical analysis of the report is followed by a secondary analysis of the key peer-reviewed article upon which the Florida Medicaid authors relied to make the decision. The paper culminates with a summation of the trajectory of anti-transgender misinformation. Conclusion: Misinformation plays a key role in producing legislation harmful to transgender people. Health science librarians have a role to play in identifying misinformation as it flows through the Misinformation - Legislation Pipeline and enacting key practices to identify, analyze, and oppose the spread of harmful misinformation. [ABSTRACT FROM AUTHOR]

Introduction: Traditional citation analysis has been greatly criticized because the process of citation accumulation requires considerable time after publication. So, the term "altmetrics" was proposed in 2010 to measure the scientific and social impact of a paper. We performed a search for certain nuclear medicine topics using the altmetrics approach to report the correlation between the altmetrics index and the number of citations. Methods: In this descriptive-analytical study, we retrieved the articles entitled with a few nuclear medicine keywords that published from 2010 to 2019 in the Web of Science (WoS). The number of 730 original papers included in this study. Altmetrics data were derived via an altmetrics bookmarklet. Statistical analysis was performed to measure the correlation between the altmetrics score and the citation count of nuclear medicine papers. Results: Mendeley and Twitter had the highest score of attention on social media platforms. Demographic information revealed that the most number of tweets and Mendeley's attention in nuclear medicine belonged to the United States (US). Moreover, researchers had the highest rate of shares in Mendeley. The correlation between the altmetrics score and citation index was significant (p <0.05). Conclusion: The authors have to pay more attention to social activities on the web for wide dissemination and proper evaluation of their scientific publications. [ABSTRACT FROM AUTHOR]

Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic's most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents' opinions on critical issues. [ABSTRACT FROM AUTHOR]

Traditional citation analysis has been greatly criticized because the process of citation accumulation requires considerable time after publication. So, the term "altmetrics" was proposed in 2010 to measure the scientific and social impact of a paper. According to the deficiencies of traditional citation analysis, we performed a comprehensive search for medical ethics publications using the altmetrics approach from the beginning until 2019. In this descriptive-analytical study, we retrieved the articles discussing any topics relating to medical ethics that published in the Scopus database from the beginning till 2019 using related medical ethics keywords. A total number of 455 articles with altmetrics scores and citations, included in this study. Altmetrics data were extracted via an altmetrics bookmarklet. Dimensions, Mendeley, and Twitter, had prominent sources of attention on social media platforms. The most number of tweets, and Mendeley's attentions, in the medical ethics fields, originated from the United States (US) and the United Kingdom (UK). Moreover, master students have the largest share in the citation of articles in Mendeley. Journal of Medical Ethics and History of Medicine has the most proportion of altmetrics score among Iranian papers in medical ethics. The correlation between the altmetrics score and citation index was significant (p <0.05). The medical ethics researchers have to pay more attention to social activities (such as creating and updating their profiles on social media) on the web for wide dissemination and proper evaluation of their scientific publications. [ABSTRACT FROM AUTHOR]

Background: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.Methods: We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.Results: Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.Conclusions: BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions. [ABSTRACT FROM AUTHOR]

This paper seeks to shed light on the ways people of color, in the United States, are using social media to challenge racial bias. As part of this investigation, this paper draws on Critical Race Theory, Feminist Theory, and Digital New Media studies to examine the extent to which social media, while seen as a place for 'play' can also operate as a digital homespace, a space used as a tool for black women and men to (re)construct their bodies and identities, challenging the "controlling images" widespread in mainstream media and society at large. This paper employs the methods of content analysis and participant observation and find that these social media forums are not transformative by themselves but instead have little moments that make for resistance and a digital homespace. [ABSTRACT FROM AUTHOR]

Introduction. The mixed experience of business intelligence implementation is shifting the focus of researchers and practitioners from technology issues to human factors. The wide set of human factors influencing business intelligence adoption tends to define features of what may be called business intelligence culture, a concept covering human and managerial issues that emerge in business intelligence implementation. The goal of this paper is to point out the key features of business intelligence culture, and examine their relations to the perceived level of this culture. Method. A test survey has been performed in a group of actual users of business intelligence systems to define specific groups of human factors that may be considered the elements of business intelligence culture. Analysis. Relations between key features of business intelligence culture and perceived level of this culture have been evaluated using IBM SPSS Modeler data mining software. Results. Key features of business intelligence culture have been defined, and a relation of some of these features, mostly information sharing, to the perceived level of business intelligence culture has been evaluated. Conclusions. The research has shown that the key features of business intelligence culture are sharing of information and insights, creation of intelligence community, and information. The dominating number of respondents relate perceived level of business intelligence culture to such key features as information sharing. [ABSTRACT FROM AUTHOR]

Objective This paper identifies general properties of language style in social media to help identify areas of need in disasters. Background In the search for metrics of need in social media data, much of the existing literature ignores processes of language usage. Psychological concepts, such as narrative breach, Gricean maxims, and lexical marking in cognition, may assist the recovery of disaster-relevant metrics from altered patterns of word prevalence. Method We analyzed several hundred thousand location-specific microblogs from Twitter for Hurricane Sandy, Oklahoma tornadoes, and the Boston Marathon bombing along with a fantasy football control corpus, examining the relative frequency of words in 36 antonym pairs. We compared the ratio of words within these pairs to the corresponding ratios recovered from an online word norm database. Results Partial rank correlation values between observed antonym ratios demonstrate consistent patterns across disasters. For Hurricane Sandy data, 25 antonym pairs have moderate to large effect sizes for discrepancies between observed and normative ratios. Across disasters, 7 pairs are stable and meet effect size criteria. Sentiment analysis, supplementary word frequency counts with respect to disaster proximity, and examples support a "breach" account for the observed results. Conclusion Lexical choice between antonyms, only somewhat related to sentiment, suggests that social media capture wide-ranging breaches of normal functioning. Application Antonym selection contributes to screening tools based on language style for identifying relevant content and quantifying disruption using social media without the a priori specification of content keywords. [ABSTRACT FROM AUTHOR]

In life-threatening conditions such as cancer and rare diseases, where there is no cure and no U.S. Food and Drug Administration (FDA)-approved therapy, patients sometimes seek access to an unapproved, experimental therapy through expanded access programs as their last, best hope for treatment to save their lives. Since the 1980s, the policies and the practice of expanded access have evolved, but a common challenge remains that there is no obligation, and often little incentive, for manufacturers to offer expanded access programs, especially for individual patients. In recent years, online campaigns seeking access to an experimental therapy have become more common, paralleling growth in and representing an intersection of social media, digital health, and patient advocacy.Mackey and Schoenfeld have examined the evolution of expanded access policy, practice, and trends, as well as case studies of online campaigns to access experimental therapies, to arrive at several recommendations for the future of expanded access. This commentary puts their paper in context, examines their recommendations, and suggests further reforms.Please see related article: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-016-0568-8. [ABSTRACT FROM AUTHOR]

This study focuses on second screening for news, a hybrid media process that combines watching news on television and a second, web-connected screen (i.e., smart-phone, laptop). Based on U.S.-national, 2-wave panel data, the paper (a) examines people's motivations to engage in second screen use, and then (b) advances the relationship between second screening and online political behaviors. Discussing and pursuing further information are both central motivations for second screen use. Furthermore, results suggest second screening for news is a significant predictor of online political participation and a key link between TV news and political engagement as this relationship is fully mediated. The study advances theoretically and empirically ways in which an informed public opinion may partake of a more engaged democracy. [ABSTRACT FROM AUTHOR]

Huge cost pressures and the need to drive faster approvals has driven a technology transformation in the clinical trial (CT) industry. The CT industry is thus leveraging mobile data, cloud computing, social media, robotic automation, and electronic source to drive efficiencies in a big way. Outsourcing of clinical operations support services to technology companies with a clinical edge is gaining tremendous importance. This paper provides an overview of current technology trends, applicable Food and Drug Administration (FDA) guidelines, basic challenges that the pharma industry is facing in trying to implement such changes and its shift towards outsourcing these services to enable it to focus on site operations. [ABSTRACT FROM AUTHOR]

Due to the hybrid nature of material and digital spaces, more decisions are being made online that have a direct effect on offline actions. This is increasingly true for the locations where people are choosing to consume goods and services such as restaurants or retail outlets. The growth of the GeoWeb-personal data uploaded to certain Internet sites such as social media platforms-has established large databases showing the locations where people go during their daily lives for the purposes of consumption. One such repository is the social network, Foursquare, which people use to display their physical location to their friends, digitally. In looking more closely at datasets from Foursquare overlaid with information on racial characteristics in census tracts, a pattern emerges: predominantly African-American tracts are increasingly left out of this type of online participation. This paper will compare Foursquare data from several U.S. cities to discuss the implications of being left off of social media platforms tied to economic activity. It is likely that these virtually invisible areas will have a direct impact on the economic vitality of their physical counterparts [ABSTRACT FROM AUTHOR]

Background: Public health and governmental organizations are expected to provide guidance to the public on emerging health issues in accessible formats. It is, therefore, important to examine how such organizations are discussing cannabis online and the information that is being provided to the public about this increasingly legal and available substance.Methods: This paper presents a concise thematic analysis of both the volume and content of cannabis-related health information from selected (n = 13) national-level public health and governmental organizations in Canada and the U.S. on Twitter.Results: There were eight themes identified in Tweets including 1) health-related topics; 2) legalization and legislation; 3) research on cannabis; 4) special populations; 5) driving and cannabis; 6) population issues; 7) medical cannabis, and 8) public health issues. The majority of cannabis-related Tweets from the organizations studied came from relatively few organizations and there were substantial differences between the topics covered by U.S. and Canadian organizations. The organizations studied provided limited information regarding how to use cannabis in ways that will minimize health-related harms.Conclusions: Authoritative organizations that deal with public health may consider designing timely social media communications with emerging cannabis-related information, to benefit a general public otherwise exposed to primarily pro-cannabis content on Twitter. [ABSTRACT FROM AUTHOR]

Background: Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, A lack of resources to support time- and labor- intensive diagnostic and reporting processes make it difficult establishing region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people's individual and subjective thoughts and feelings during heat waves. This study aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes.Methods: We collected Twitter messages that mentioned "air conditioning (AC)" and "heat" and Google search data that included weather, medical, recreational, and adaptation information from May 7 to November 3, 2014, focusing on the state of Florida, U.S. We separately associated web data against two different sources of health outcomes (emergency department (ED) and hospital admissions) and five disease categories (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease). Seasonal and subseasonal temporal cycles were controlled using autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) and generalized linear model (GLM).Results: The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, we found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases.Conclusions: Our findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This paper also shows that activity patterns for reducing heat stress are associated with several health outcomes. Based on the results, we believe web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective. [ABSTRACT FROM AUTHOR]