Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis

BACKGROUND

The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels.

OBJECTIVE

Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti-HIV vaccine conspiracy theories through manual coding.

METHODS

We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti-HIV vaccine conspiracy theories.

RESULTS

Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19-related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti-HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events.

CONCLUSIONS

The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti-HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

Topic modeling on clinical social work notes for exploring social determinants of health factors

Objective

Existing research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.

Materials and methods

We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.

Results

Word frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.

Discussion

Our findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.

Conclusion

Social work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health

Objectives

To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI).

Materials and Methods

Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI.

Results

Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data.

Discussion and Conclusion

Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study

BACKGROUND

Hypertension significantly impacts the well-being and health of individuals globally. Hypertension management apps (HMAs) have been shown to assist patients in controlling blood pressure (BP), with their efficacy validated in clinical trials. However, the utilization of HMAs continues to be suboptimal. Presently, there is a dearth of real-world research based on big data and exploratory mining that compares Chinese and American HMAs.

OBJECTIVE

This study aims to systematically gather HMAs and their user reviews from both China and the United States. Subsequently, using data mining techniques, the study aims to compare the user experience, satisfaction levels, influencing factors, and asymmetry between Chinese and American users of HMAs. In addition, the study seeks to assess the disparities in satisfaction and its determinants while delving into the asymmetry of these factors.

METHODS

The study sourced HMAs and user reviews from 10 prominent Chinese and American app stores globally. Using the latent Dirichlet allocation (LDA) topic model, the research identified various topics within user reviews. Subsequently, the Tobit model was used to investigate the impact and distinctions of each topic on user satisfaction. The Wald test was applied to analyze differences in effects across various factors.

RESULTS

We examined a total of 261 HMAs along with their associated user reviews, amounting to 116,686 reviews in total. In terms of quantity and overall satisfaction levels, Chinese HMAs (n=91) and corresponding reviews (n=16,561) were notably fewer compared with their American counterparts (n=220 HMAs and n=100,125 reviews). The overall satisfaction rate among HMA users was 75.22% (87,773/116,686), with Chinese HMAs demonstrating a higher satisfaction rate (13,866/16,561, 83.73%) compared with that for American HMAs (73,907/100,125, 73.81%). Chinese users primarily focus on reliability (2165/16,561, 13.07%) and measurement accuracy (2091/16,561, 12.63%) when considering HMAs, whereas American users prioritize BP tracking (17,285/100,125, 17.26%) and data synchronization (12,837/100,125, 12.82%). Seven factors (easy to use: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; heart rate detection function: P=.02; blood pressure tracking function: P<.001; and interface design: P=.01) significantly influenced the positive deviation (PD) of Chinese HMA user satisfaction, while 8 factors (easy to use: P<.001; reliability: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; interface design: P<.001; real-time: P<.001; and data privacy: P=.001) affected the negative deviation (ND). Notably, BP tracking had the greatest effect on PD (β=.354, P<.001), while cost had the most significant impact on ND (β=3.703, P<.001). All 12 factors (easy to use: P<.001; blood pressure tracking function: P<.001; data synchronization: P<.001; blood pressure management effect: P<.001; heart rate detection function: P<.001; data sharing: P<.001; reliability: P<.001; compatibility: P<.001; interface design: P<.001; advertisement distribution: P<.001; measurement accuracy: P<.001; and cost: P<.001) significantly influenced the PD and ND of American HMA user satisfaction. Notably, BP tracking had the greatest effect on PD (β=0.312, P<.001), while data synchronization had the most significant impact on ND (β=2.662, P<.001). In addition, the influencing factors of PD and ND in user satisfaction of HMA in China and the United States are different.

CONCLUSIONS

User satisfaction factors varied significantly between different countries, showing considerable asymmetry. For Chinese HMA users, ease of use and interface design emerged as motivational factors, while factors such as cost, measurement accuracy, and compatibility primarily contributed to user dissatisfaction. For American HMA users, motivational factors were ease of use, BP tracking, BP management effect, interface design, measurement accuracy, and cost. Moreover, users expect features such as data sharing, synchronization, software reliability, compatibility, heart rate detection, and nonintrusive advertisement distribution. Tailored experience plans should be devised for different user groups in various countries to address these diverse preferences and requirements.

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

BACKGROUND

Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited.

OBJECTIVE

This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security.

METHODS

A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years.

RESULTS

In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues.

CONCLUSIONS

In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

Key point generation as an instrument for generating core statements of a political debate on Twitter

Identifying key statements in large volumes of short, user-generated texts is essential for decision-makers to quickly grasp their key content. To address this need, this research introduces a novel abstractive key point generation (KPG) approach applicable to unlabeled text corpora, using an unsupervised approach, a feature not yet seen in existing abstractive KPG methods. The proposed method uniquely combines topic modeling for unsupervised data space segmentation with abstractive summarization techniques to efficiently generate semantically representative key points from text collections. This is further enhanced by hyperparameter tuning to optimize both the topic modeling and abstractive summarization processes. The hyperparameter tuning of the topic modeling aims at making the cluster assignment more deterministic as the probabilistic nature of the process would otherwise lead to high variability in the output. The abstractive summarization process is optimized using a Davies-Bouldin Index specifically adapted to this use case, so that the generated key points more accurately reflect the characteristic properties of this cluster. In addition, our research recommends an automated evaluation that provides a quantitative complement to the traditional qualitative analysis of KPG. This method regards KPG as a specialized form of Multidocument summarization (MDS) and employs both word-based and word-embedding-based metrics for evaluation. These criteria allow for a comprehensive and nuanced analysis of the KPG output. Demonstrated through application to a political debate on Twitter, the versatility of this approach extends to various domains, such as product review analysis and survey evaluation. This research not only paves the way for innovative development in abstractive KPG methods but also sets a benchmark for their evaluation.

Public perception on active aging after COVID-19: an unsupervised machine learning analysis of 44,343 posts

Introduction

To analyze public perceptions of active aging in China on mainstream social media platforms to determine whether the "14th Five Year Plan for the Development of the Aging Career and Older Adult Care System" issued by the CPC in 2022 has fully addressed public needs.

Methods

The original tweets posted on Weibo between January 1, 2020, and June 30, 2022, containing the words "aging" or "old age" were extracted. A bidirectional encoder representation from transformers (BERT)-based model was used to generate themes related to this perception. A qualitative thematic analysis and an independent review of the theme labels were conducted by the researchers.

Results

The findings indicate that public perceptions revolved around four themes: (1) health prevention and protection, (2) convenient living environments, (3) cognitive health and social integration, and (4) protecting the rights and interests of the older adult.

Discussion

Our study found that although the Plan aligns with most of these themes, it lacks clear planning for financial security and marital life.

Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research

Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.

Enhancing public health response: a framework for topics and sentiment analysis of COVID-19 in the UK using Twitter and the embedded topic model

Introduction

To protect citizens during the COVID-19 pandemic unprecedented public health restrictions were imposed on everyday life in the UK and around the world. In emergencies like COVID-19, it is crucial for policymakers to be able to gauge the public response and sentiment to such measures in almost real-time and establish best practices for the use of social media for emergency response.

Methods

In this study, we explored Twitter as a data source for assessing public reaction to the pandemic. We conducted an analysis of sentiment by topic using 25 million UK tweets, collected from 26th May 2020 to 8th March 2021. We combined an innovative combination of sentiment analysis via a recurrent neural network and topic clustering through an embedded topic model.

Results

The results demonstrated interpretable per-topic sentiment signals across time and geography in the UK that could be tied to specific public health and policy events during the pandemic. Unique to this investigation is the juxtaposition of derived sentiment trends against behavioral surveys conducted by the UK Office for National Statistics, providing a robust gauge of the public mood concurrent with policy announcements.

Discussion

While much of the existing research focused on specific questions or new techniques, we developed a comprehensive framework for the assessment of public response by policymakers for COVID-19 and generalizable for future emergencies. The emergent methodology not only elucidates the public's stance on COVID-19 policies but also establishes a generalizable framework for public policymakers to monitor and assess the buy-in and acceptance of their policies almost in real-time. Further, the proposed approach is generalizable as a tool for policymakers and could be applied to further subjects of political and public interest.

Understanding global research trends in the control and prevention of infectious diseases for children: Insights from text mining and topic modeling

INTRODUCTION

The emergence of novel infectious diseases has amplified the urgent need for effective prevention strategies, especially ones targeting vulnerable populations such as children. Factors such as the high incidence of both emerging and existing infectious diseases, delays in vaccinations, and routine exposure in communal settings heighten children's susceptibility to infections. Despite this pressing need, a comprehensive exploration of research trends in this domain remains lacking. This study aims to address this gap by employing text mining and modeling techniques to conduct a comprehensive analysis of the existing literature, thereby identifying emerging research trends in infectious disease prevention among children.

METHODS

A cross-sectional text mining approach was adopted, focusing on journal articles published between January 1, 2003, and August 31, 2022. These articles, related to infectious disease prevention in children, were sourced from databases such as PubMed, CINAHL, MEDLINE (Ovid), Scopus, and Korean RISS. The data underwent preprocessing using the Natural Language Toolkit (NLTK) in Python, with a semantic network analysis and topic modeling conducted using R software.

RESULTS

The final dataset comprised 509 journal articles extracted from multiple databases. The study began with a word frequency analysis to pinpoint relevant themes, subsequently visualized through a word cloud. Dominant terms encompassed "vaccination," "adolescent," "infant," "parent," "family," "school," "country," "household," "community," "HIV," "HPV," "COVID-19," "influenza," and "diarrhea." The semantic analysis identified "age" as a key term across infection, control, and intervention discussions. Notably, the relationship between "hand" and "handwashing" was prominent, especially in educational contexts linked with "school" and "absence." Latent Dirichlet Allocation (LDA) topic modeling further delineated seven topics related to infectious disease prevention for children, encompassing (1) educational programs, (2) vaccination efforts, (3) family-level responses, (4) care for immunocompromised individuals, (5) country-specific responses, (6) school-based strategies, and (7) persistent threats from established infectious diseases.

CONCLUSION

The study emphasizes the indispensable role of personalized interventions tailored for various child demographics, highlighting the pivotal contributions of both parental guidance and school participation.

CLINICAL RELEVANCE

The study provides insights into the complex public health challenges associated with preventing and managing infectious diseases in children. The insights derived could inform the formulation of evidence-based public health policies, steering practical interventions and fostering interdisciplinary synergy for holistic prevention strategies.

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Introduction

The utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.

Methods

Twitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.

Results

The NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.

Discussion

The study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

The Marketing and Perceptions of Non-Tobacco Blunt Wraps on Twitter

OBJECTIVE

Non-tobacco blunt wraps (N-TBWs), which entered the marketplace in 2017, are being promoted as an alternative to traditional TBWs (e.g., cigarillos) for blunt smoking. The lack of studies on these novel products warrants an investigation. This study was the first to explore blunt smokers' perceptions about N-TBWs and the extent of product marketing on Twitter.

METHODS

A corpus of tweets from Twitter, posted between January 2017 and November 2021, were identified by a Boolean search string (N = 149,343), where 48,695 tweets were classified as relevant by a machine learning algorithm. These relevant tweets were further screened and labeled as promotional or organic based on product URLs, usernames, keywords, or hashtags. Topic modeling using Dirichlet Allocation was then employed for identifying latent patterns of words among relevant tweets. The Social Networking Potential (SNP) score was employed for identifying influential accounts.

RESULTS

Most relevant tweets (89%) were organic, non-promotional expressions about N-TBWs. Account users who only posted non-promotional tweets had a significantly higher SNP than those who only posted promotional tweets. Yet, neither of the two groups of account users consisted of known celebrities. Topic modeling revealed three broad groups of topics (7 in total) denoting the attributes of hemp N-TBWs, interest in non-hemp N-TBWs, and product marketing.

CONCLUSIONS

The large proportion of organic tweets is indicative of the nascency of N-TBWs, which will need to be marketed more extensively if they are to replace cigar products used by blunt smokers.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media

BACKGROUND

The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic.

OBJECTIVE

Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion.

METHODS

First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model.

RESULTS

The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive.

CONCLUSIONS

The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.

Impact of Program Components on Perceived Organizational Support in Respiratory Care Education

BACKGROUND

Perceived organizational support has been linked to employee commitment and job satisfaction. Understanding the effects of perceived organizational support on employees allows leaders to improve employees' performance and the success of their organizations. The purpose of this study was to identify the perceived organizational support across different respiratory care education programs in the United States.

METHODS

All chairs and program directors of bachelor's of science and master's of science degree respiratory care education programs in the United States were surveyed (N = 97). The Survey of Perceived Organizational Support was modified after written approval, and the final instrument included 31 items with a Likert scale (1 = strongly disagree, 7 = strongly agree). Descriptive statistics, multiple regression, and topic modeling were used for data analysis (P < .05).

RESULTS

A total of 67 respondents responded to the perceived organizational support survey; a 69% response rate. They were satisfied with their job and committed to their institutions. They also reported that faculty salaries were equitable relative to the national average, and their institutions encouraged teamwork among faculty. The respondents' titles, total years of administrative experience, students' scores on the national credentialing therapist multiple choice examination (TMC), and institutions that offer both bachelor's of science and master's of science degree programs had a direct relationship with perceived organizational support in respiratory care education programs. Age and sex were inversely related to perceived organizational support. A topic modeling analysis based on the respondents' opinions about perceived organizational support showed that the respondents frequently mentioned the words support, institution, budget, year, nursing, and experience. The respondents emphasized the importance of support, institution marketing, their years of experience, and the program budget. They also mentioned that nursing programs overshadowed respiratory care education programs at their institutions.

CONCLUSIONS

Age, sex, job title, years of administrative experience, students' TMC scores, and the type of programs offered impacted perceived organizational support by respiratory care directors. Student-, program- and participant-related factors can be used to improve perceived organizational support in respiratory care education.

Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study

BACKGROUND

Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies.

OBJECTIVE

This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies.

METHODS

Two types of data were collected: (1) a total of 17,524 COVID-19-related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19-related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data.

RESULTS

Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data.

CONCLUSIONS

Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.

Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.

How satisfied are patients with nursing care and why? A comprehensive study based on social media and opinion mining

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonetheless, one of the key aspects is the performance of nurses as they closely interact with patients on many occasions. Nurses carry out many tasks that could be assessed to understand the patient's satisfaction and consequently, the effectiveness of the offered services. To assess their performance, traditionally, expensive, and time-consuming methods such as questionnaires and interviews have been used; nevertheless, the development of social networks has allowed the patients to convey their opinions in a free and public manner. For that reason, in this study, a comprehensive analysis has been performed based on patients' opinions collected from a feedback platform for health and care services, to discover the topics about nurses the patients are more interested in. To do so, a topic modeling technique has been proposed. After this, sentiment analysis has been applied to classify the topics as satisfactory or unsatisfactory. Finally, the results have been compared with what the patients think about doctors. The results highlight what topics are most relevant to assess the patient satisfaction and to what extent. The results remark that the opinion about nurses is, in general, more positive than about doctors.

Sentiment analysis of the COVID-19 vaccine perception

The sharp rise in coronavirus cases in the United States, as well as other countries, is driven by variants such as the Omicron substrain, BA4 and BA5. Keeping up to date with COVID-19 vaccination and wearing masks are essential tools for mitigating the pandemic. Social media plays a vital role in sharing and exchanging information, but it also affects perceptions of social phenomena. In this study, we conducted sentiment analysis and topic modeling to investigate vaccine perception using 338,465 COVID-19 vaccine-related comments collected from January 2020 to May 2021 on Reddit. This study stands apart from prior COVID-related research on social media, particularly on Reddit, as it conducted separate analyses for each COVID vaccine and examines public sentiment with various societal events, including vaccine development progress and government responses to COVID. The findings reveal two notable spikes in the number of comments containing the keyword "vaccine". This suggests that discussions about vaccines tend to increase during times of significant social and political events, indicating that people's attention and interest in the topic are influenced by current events. Understanding the public perception of vaccines and identifying factors influencing vaccine perception could help propose appropriate interventions to promote vaccination.

Navigating the Landscape of Telemedicine Research: A Topic Modeling Approach for the Present and Future

Introduction: Telemedicine, which is the provision of remote clinical services via telecommunication technology, has undergone an upsurge since the COVID-19 pandemic. To capture this paradigm, this study surveyed telemedicine literature, including postpandemic publications, to identify dominant research themes and temporal trends and suggest directions for future research. Methods: A corpus of 56,445 telemedicine studies is sourced from PubMed. Latent Dirichlet allocation (LDA) topic modeling performed using the Konstanz Information Miner platform. The textual data for topic modeling were processed by following standard procedures for natural language processing. Moreover, the term frequency-inverse document frequency approach was used to capture the importance of words within the corpus. We assessed perplexity, coherence, and the elbow method to determine the optimal number of topics for modeling. Results: The findings confirm the surge in telemedicine research after 2020, signifying its prominence. LDA topic modeling reveals seven distinct research themes, with the most prominent topic being "patient satisfaction" (21.38%) followed by "perspectives and challenges" (17.95%), and "smartphone apps" (14.32%). Furthermore, the results demonstrate a noticeable shift in topics from screening to therapeutic applications of telemedicine. Conclusions: This study serves as a guide for a broad range of telemedicine research topics. This synthesis of themes reflects the commitment of scholars to address the changing dynamics and health care needs, such as the COVID-19 pandemic, aging in place, smartphone usage, and technological advancement. The analysis also reveals flexible research responses to policy and contextual shifts, highlighting the collective drive to broaden the application of telemedicine in community health care.

Using Transformer-Based Topic Modeling to Examine Discussions of Delta-8 Tetrahydrocannabinol: Content Analysis

BACKGROUND

Delta-8 tetrahydrocannabinol (THC) is a psychoactive cannabinoid found in small amounts naturally in the cannabis plant; it can also be synthetically produced in larger quantities from hemp-derived cannabidiol. Most states permit the sale of hemp and hemp-derived cannabidiol products; thus, hemp-derived delta-8 THC products have become widely available in many state hemp marketplaces, even where delta-9 THC, the most prominently occurring THC isomer in cannabis, is not currently legal. Health concerns related to the processing of delta-8 THC products and their psychoactive effects remain understudied.

OBJECTIVE

The goal of this study is to implement a novel topic modeling approach based on transformers, a state-of-the-art natural language processing architecture, to identify and describe emerging trends and topics of discussion about delta-8 THC from social media discourse, including potential symptoms and adverse health outcomes experienced by people using delta-8 THC products.

METHODS

Posts from January 2008 to December 2021 discussing delta-8 THC were isolated from cannabis-related drug forums on Reddit (Reddit Inc), a social media platform that hosts the largest web-based drug forums worldwide. Unsupervised topic modeling with state-of-the-art transformer-based models was used to cluster posts into topics and assign labels describing the kinds of issues being discussed with respect to delta-8 THC. Results were then validated by human subject matter experts.

RESULTS

There were 41,191 delta-8 THC posts identified and 81 topics isolated, the most prevalent being (1) discussion of specific brands or products, (2) comparison of delta-8 THC to other hemp-derived cannabinoids, and (3) safety warnings. About 5% (n=1220) of posts from the resulting topics included content discussing health-related symptoms such as anxiety, sleep disturbance, and breathing problems. Until 2020, Reddit posts contained fewer than 10 mentions of delta-8-THC for every 100,000 cannabis posts annually. However, in 2020, these rates increased by 13 times the 2019 rate (to 99.2 mentions per 100,000 cannabis posts) and continued to increase into 2021 (349.5 mentions per 100,000 cannabis posts).

CONCLUSIONS

Our study provides insights into emerging public health concerns around delta-8 THC, a novel substance about which little is known. Furthermore, we demonstrate the use of transformer-based unsupervised learning approaches to derive intelligible topics from highly unstructured discussions of delta-8 THC, which may help improve the timeliness of identification of emerging health concerns related to new substances.

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

BACKGROUND

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners.

OBJECTIVE

In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research.

METHODS

Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy.

RESULTS

From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine.

CONCLUSIONS

The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Listen to the patients! Identifying CML patients' needs analyzing patient-generated content with AI-driven methodologies

Background

Various patient support programs exist to provide successful therapy options for patients. Pharmaceutical companies are increasingly recognizing the importance of actively supporting patients in their long-term treatment. In order to effectively assist patients, it is crucial to understand their current needs by taking a look at the patients' opinions.

Objective

This study focuses specifically on chronic myeloid leukemia (CML) and aims to determine if the current patient engagement offerings from pharmaceutical companies adequately address the needs of CML patients. To achieve this, the study uses content generated by CML patients to assess the patient engagement strategies of selected pharmaceutical companies, explore the relevance of medication, their products, and services, and analyze key concerns from the perspective of the patients.

Methods

To address the research questions, various methodologies were employed. Initially, desk research was conducted to identify relevant pharmaceutical companies and internet forums related to CML. Subsequently, content generated by patients was acquired and AI-driven techniques such as topic modeling and topic evolution analyses were used to examine this user-generated content (UGC) within the identified public forums. This involved analyzing topic models and tracking topic changes over time.

Results

The desk research revealed that pharmaceutical companies primarily offer information about the disease and available treatment options. The UGC analysis confirmed the significant role played by the industry in supporting CML patients. Key areas of interest for patients include the disease itself, potential treatment methods and associated side effects, dosage of active substances, and the possibility of switching therapies due to treatment failure or resistance. Stem cell transplantation was also discussed.

Conclusions

Overall, the pharmaceutical industry adequately addresses the needs of CML patients. However, there is room for improvement in educating patients about treatment options, drugs, and their side effects. Psychological support should not be neglected. Since CML patients frequently engage with clinical trial outcomes, there is potential for increased patient involvement in such trials. Further research in this area is recommended.

Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit

BACKGROUND

Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation.

OBJECTIVE

The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation.

METHODS

We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts.

RESULTS

A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting.

CONCLUSIONS

Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.

Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community

BACKGROUND

Alzheimer disease or related dementias (ADRD) are severe neurological disorders that impair the thinking and memory skills of older adults. Most persons living with dementia receive care at home from their family members or other unpaid informal caregivers; this results in significant mental, physical, and financial challenges for these caregivers. To combat these challenges, many informal ADRD caregivers seek social support in online environments. Although research examining online caregiving discussions is growing, few investigations have distinguished caregivers according to their kin relationships with persons living with dementias. Various studies have suggested that caregivers in different relationships experience distinct caregiving challenges and support needs.

OBJECTIVE

This study aims to examine and compare the online behaviors of adult-child and spousal caregivers, the 2 largest groups of informal ADRD caregivers, in an open online community.

METHODS

We collected posts from ALZConnected, an online community managed by the Alzheimer's Association. To gain insights into online behaviors, we first applied structural topic modeling to identify topics and topic prevalence between adult-child and spousal caregivers. Next, we applied VADER (Valence Aware Dictionary for Sentiment Reasoning) and LIWC (Linguistic Inquiry and Word Count) to evaluate sentiment changes in the online posts over time for both types of caregivers. We further built machine learning models to distinguish the posts of each caregiver type and evaluated them in terms of precision, recall, F1-score, and area under the precision-recall curve. Finally, we applied the best prediction model to compare the temporal trend of relationship-predicting capacities in posts between the 2 types of caregivers.

RESULTS

Our analysis showed that the number of posts from both types of caregivers followed a long-tailed distribution, indicating that most caregivers in this online community were infrequent users. In comparison with adult-child caregivers, spousal caregivers tended to be more active in the community, publishing more posts and engaging in discussions on a wider range of caregiving topics. Spousal caregivers also exhibited slower growth in positive emotional communication over time. The best machine learning model for predicting adult-child, spousal, or other caregivers achieved an area under the precision-recall curve of 81.3%. The subsequent trend analysis showed that it became more difficult to predict adult-child caregiver posts than spousal caregiver posts over time. This suggests that adult-child and spousal caregivers might gradually shift their discussions from questions that are more directly related to their own experiences and needs to questions that are more general and applicable to other types of caregivers.

CONCLUSIONS

Our findings suggest that it is important for researchers and community organizers to consider the heterogeneity of caregiving experiences and subsequent online behaviors among different types of caregivers when tailoring online peer support to meet the specific needs of each caregiver group.

Sentiments about Mental Health on Twitter-Before and during the COVID-19 Pandemic

During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.

Identifying mental health discussion topic in social media community: subreddit of bipolar disorder analysis

Online platforms allow individuals to connect with others, share experiences, and find communities with similar interests, providing a sense of belonging and reducing feelings of isolation. Numerous previous studies examined the content of online health communities to gain insights into the sentiments surrounding mental health conditions. However, there is a noticeable gap in the research landscape, as no study has specifically concentrated on conducting an in-depth analysis or providing a comprehensive visualization of Bipolar disorder. Therefore, this study aimed to address this gap by examining the Bipolar subreddit online community, where we collected 1,460,447 posts as plain text documents for analysis. By employing LDA topic modeling and sentiment analysis, we found that the Bipolar disorder online community on Reddit discussed various aspects of the condition, including symptoms, mood swings, diagnosis, and medication. Users shared personal experiences, challenges, and coping strategies, seeking support and connection. Discussions related to therapy and medication were prevalent, emphasizing the importance of finding suitable therapists and managing medication side effects. The online community serves as a platform for seeking help, advice, and information, highlighting the role of social support in managing bipolar disorder. This study enhances our understanding of individuals living with bipolar disorder and provides valuable insights and feedback for researchers developing mental health interventions.

Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study

The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include "Tumor Vascularity", "Stroke Assessment", "Myocardial Perfusion", "Intracerebral Hemorrhage", "Imaging Optimization", "Reperfusion Therapy", "Postprocessing", "Carotid Artery Disease", "Seizures", "Hemorrhagic Transformation", "Artificial Intelligence", and "Moyamoya Disease". The model provided insights into the trends of the current decade, highlighting "Postprocessing" and "Artificial Intelligence" as the most trending topics.

Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study

BACKGROUND

After 3 years of its zero-COVID policy, China lifted its stringent pandemic control measures with the announcement of the 10 new measures on December 7, 2022. Existing estimates suggest 90%-97% of the total population was infected during December. This change created a massive demand for COVID-19 medications and treatments, either modern medicines or traditional Chinese medicine (TCM).

OBJECTIVE

This study aimed to explore (1) how China's exit from the zero-COVID policy impacted media and the public's attention to COVID-19 medications; (2) how social COVID-19 medication discussions were related to existing model estimates of daily cases during that period; (3) what the diversified themes and topics were and how they changed and developed from November 1 to December 31, 2022; and (4) which topics about COVID-19 medications were focused on by mainstream and self-media accounts during the exit. The answers to these questions could help us better understand the consequences of exit strategies and explore the utilities of Sina Weibo data for future infoveillance studies.

METHODS

Using a scrapper for data retrieval and the structural topic modeling (STM) algorithm for analysis, this study built 3 topic models (all data, before a policy change, and after a policy change) of relevant discussions on the Chinese social media platform Weibo. We compared topic distributions against existing estimates of daily cases and between models before and after the change. We also compared proportions of weibos published by mainstream versus self-media accounts over time on different topics.

RESULTS

We found that Weibo discussions shifted sharply from concerns of social risks (case tracking, governmental regulations, etc) to those of personal risks (symptoms, purchases, etc) surrounding COVID-19 infection after the exit from the zero-COVID policy. Weibo topics of "symptom sharing" and "purchase and shortage" of modern medicines correlated more strongly with existing susceptible-exposed-infected-recovered (SEIR) model estimates compared to "TCM formulae" and other topics. During the exit, mainstream accounts showed efforts to specifically engage in topics related to worldwide pandemic control policy comparison and regulations about import and reimbursement of medications.

CONCLUSIONS

The exit from the zero-COVID policy in China was accompanied by a sudden increase in social media discussions about COVID-19 medications, the demand for which substantially increased after the exit. A large proportion of Weibo discussions were emotional and expressed increased risk concerns over medication shortage, unavailability, and delay in delivery. Topic keywords showed that self-medication was sometimes practiced alone or with unprofessional help from others, while mainstream accounts also tried to provide certain medication instructions. Of the 16 topics identified in all 3 STM models, only "symptom sharing" and "purchase and shortage" showed a considerable correlation with SEIR model estimates of daily cases. Future studies could consider topic exploration before conducting predictive infoveillance analysis, even with narrowly defined search criteria with Weibo data.

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis

BACKGROUND

This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes.

OBJECTIVE

The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods.

METHODS

Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance.

RESULTS

Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic.

CONCLUSIONS

The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively.

Soccer's AI transformation: deep learning's analysis of soccer's pandemic research evolution

Introduction

This paper aims to identify and compare changes in trends and research interests in soccer articles from before and during the COVID-19 pandemic.

Methods

We compared research interests and trends in soccer-related journal articles published before COVID-19 (2018-2020) and during the COVID-19 pandemic (2021-2022) using Bidirectional Encoder Representations from Transformers (BERT) topic modeling.

Results

In both periods, we categorized the social sciences into psychology, sociology, business, and technology, with some interdisciplinary research topics identified, and we identified changes during the COVID-19 pandemic period, including a new approach to home advantage. Furthermore, Sports science and sports medicine had a vast array of subject areas and topics, but some similar themes emerged in both periods and found changes before and during COVID-19. These changes can be broadly categorized into (a) Social Sciences and Technology; (b) Performance training approaches; (c) injury part of body. With training topics being more prominent than match performance during the pandemic; and changes within injuries, with the lower limbs becoming more prominent than the head during the pandemic.

Conclusion

Now that the pandemic has ended, soccer environments and routines have returned to pre-pandemic levels, but the environment that have changed during the pandemic provide an opportunity for researchers and practitioners in the field of soccer to detect post-pandemic changes and identify trends and future directions for research.

Development of a Japanese version of the Awe Experience Scale (AWE-S): A structural topic modeling approach

BACKGROUND

Awe, a complex emotion, arises in response to perceptually and conceptually vast stimuli that transcend one's current frames of reference, which is associated with subjective psychological phenomena, such as a sense of self and consciousness. This study aimed to develop a Japanese version of the Awe Experience Scale (AWE-S), a widely used questionnaire that robustly measured the state of awe, and simultaneously investigated how the multiple facets of awe related to the narrative representations of awe experiences.

METHODS

The Japanese AWE-S was created via back-translation and its factor structure and validity was investigated through an online survey in Japan.

RESULTS

The results revealed that the Japanese AWE-S consisted of the same six factors as the original (i.e., time, self-loss, connectedness, vastness, physiological, and accommodation) and had sufficient internal consistency, test-retest reliability, construct validity, and also Japan-specific characteristics. The structured topic modeling generated seven potential topics of the descriptions of awe experiences, which were differently associated with each factor of the Japanese AWE-S.

CONCLUSIONS

Our findings contribute to a deeper understanding of awe and reveal the constructs of awe in Japan through cross-cultural comparisons. Furthermore, this study provides conceptual and methodological implications regarding studies on awe.

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.

Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews

Introduction: Use of both in-person and video visits have become a common norm in health care delivery, especially after the COVID-19 pandemic. It is imperative to understand how patients feel about their providers and their experiences during in-person and video visits. This study examines the important factors that patients use in their reviews and differences in the relative importance. Methods: We performed sentiment analysis and topic modeling on online physician reviews from April 2020 to April 2022. Our dataset comprised 34,824 reviews posted by patients after completing in-person or video visits. Results: Sentiment analysis yielded 27,507 (92.69%) positive and 2,168 (7.31%) negative reviews for in-person visits, and 4,610 (89.53%) positive and 539 (10.47%) negative reviews for video visits. Topic modeling identified seven factors patients used in their reviews: Bedside manners, Medical Expertise, Communication, Visit Environment, Scheduling and Follow-up, Wait times, and Costs and insurance. Patients who gave positive reviews after in-person consultations more frequently mentioned communication, office environment and staff, and bedside manners. Those who gave negative reviews after in-person visits mentioned longer wait times, providers' office and staff, medical expertise, and costs and insurance problems. Patients with positive reviews after video visits emphasized communication, bedside manners, and medical expertise. However, patients posting negative reviews after video visits frequently mentioned problems with appointment scheduling and follow-up, medical expertise, wait times, costs and insurance, and technical problems in video visits. Conclusions: This study identified key factors that influence patients' assessment of their providers in in-person and video visits. Paying attention to these factors can help improve the overall patient experience.

Data Analysis of Psychological Approaches to Soccer Research: Using LDA Topic Modeling

This study identifies the topical areas of research that have attempted a psychological approach to soccer research over the last 33 years (1990-2022) and explored the growth and stagnation of the topic as well as research contributions to soccer development. Data were obtained from 1863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The "Coaching Essentials in Football" topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of "Psychological Skills for Performance Development" has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. "Motivation, cognition, and emotion" is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study

BACKGROUND

During the COVID-19 pandemic, web-based media coverage of preventative strategies proliferated substantially. News media was constantly informing people about changes in public health policy and practices such as mask-wearing. Hence, exploring news media content on face mask use is useful to analyze dominant topics and their trends.

OBJECTIVE

The aim of the study was to examine news related to face masks as well as to identify related topics and temporal trends in Australian web-based news media during the early COVID-19 pandemic period.

METHODS

Following data collection from the Google News platform, a trend analysis on the mask-related news titles from Australian news publishers was conducted. Then, a latent Dirichlet allocation topic modeling algorithm was applied along with evaluation matrices (quantitative and qualitative measures). Afterward, topic trends were developed and analyzed in the context of mask use during the pandemic.

RESULTS

A total of 2345 face mask-related eligible news titles were collected from January 25, 2020, to January 25, 2021. Mask-related news showed an increasing trend corresponding to increasing COVID-19 cases in Australia. The best-fitted latent Dirichlet allocation model discovered 8 different topics with a coherence score of 0.66 and a perplexity measure of -11.29. The major topics were T1 (mask-related international affairs), T2 (introducing mask mandate in places such as Melbourne and Sydney), and T4 (antimask sentiment). Topic trends revealed that T2 was the most frequent topic in January 2021 (77 news titles), corresponding to the mandatory mask-wearing policy in Sydney.

CONCLUSIONS

This study demonstrated that Australian news media reflected a wide range of community concerns about face masks, peaking as COVID-19 incidence increased. Harnessing the news media platforms for understanding the media agenda and community concerns may assist in effective health communication during a pandemic response.

Users' Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study

BACKGROUND

Endometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns.

OBJECTIVE

This study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments.

METHODS

We retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling-based methods in machine learning.

RESULTS

Since 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts.

CONCLUSIONS

Our study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

BACKGROUND

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines.

OBJECTIVE

This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets.

METHODS

We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques.

RESULTS

Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period.

CONCLUSIONS

This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine-related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.

Influence of the pandemic lockdown on Fridays for Future's hashtag activism

Social movement organizations (SMOs) increasingly rely on Twitter to create new and viral communication spaces alongside newsworthy protest events and communicate their grievance directly to the public. When the COVID-19 pandemic impeded street protests in spring 2020, SMOs had to adapt their strategies to online-only formats. We analyze the German-language Twitter communication of the climate movement Fridays for Future (FFF) before and during the lockdown to explain how SMOs adapted their strategy under online-only conditions. We collected (re-)tweets containing the hashtag #fridaysforfuture (N = 46,881 tweets, N = 225,562 retweets) and analyzed Twitter activity, use of hashtags, and predominant topics. Results show that although the number of tweets was already steadily declining before, it sharply dropped during the lockdown. Moreover, the use of hashtags changed substantially and tweets focused increasingly on thematic discourses and debates around the legitimacy of FFF, while tweets about protests and calls for mobilization decreased.

Data Analysis of Physician Competence Research Trend: Social Network Analysis and Topic Modeling Approach

BACKGROUND

Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician competency reflects social demands according to changes in the medical environment, analyzing the research trends of physician competency by period is necessary to derive major research topics for future studies. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era.

OBJECTIVE

This study aimed to analyze research trends related to physicians' competency in reflecting social needs according to changes in the medical environment.

METHODS

We used topic modeling to identify potential research topics by analyzing data from studies related to physician competency published between 2011 and 2020. We preprocessed 1354 articles and extracted 272 keywords.

RESULTS

The terms that appeared most frequently in the research related to physician competency since 2010 were knowledge, hospital, family, job, guidelines, management, and communication. The terms that appeared in most studies were education, model, knowledge, and hospital. Topic modeling revealed that the main topics about physician competency included Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. We divided the studies into 4 periods (2011-2013, 2014-2016, 2017-2019, and 2020-2021) and performed a linear regression analysis. The results showed a change in topics by period. The hot topics that have shown increased interest among scholars over time include Community-based healthcare, Career and self-management, and Continuous professional development.

CONCLUSIONS

On the basis of the analysis of research trends, it is predicted that physician professionalism and community-based medicine will continue to be studied in future studies on physician competency.

Topic Modeling Analysis of Diabetes-Related Health Information during the Coronavirus Disease Pandemic

This study aimed to provide diabetes-related health information by analyzing queries posted in the diabetes-related online community required during the COVID-19 pandemic. A total of 9156 queries from the diabetes-related online community, dated between 1 December 2019 and 3 May 2022, were used in the study. The collected data were preprocessed for bidirectional encoder representation from transformer topic modeling analysis. Topics were extracted using the class-based term frequency-inverse document frequency for nouns and verbs. From the extracted verbs, words with common definitions were subject to substitution and unification processes, which enabled the identification of multifrequent verb categories by noun topics. The following nine noun topics were extracted, in this order: dietary management, drug management, gestational and childhood diabetes, management of diabetic complications, use and cost of medical treatment, blood glucose management, exercise treatment, COVID-19 vaccine and complications, and diabetes in older adults. The top three verb categories by noun topics were permission, method, and possibility. This study provided baseline data that can be used by clinical nurses to deliver diabetes-related education and management based on information sought by patients.

A comparison of three methods to determine the subject matter in textual data

This study compares three different methods commonly employed for the determination and interpretation of the subject matter of large corpuses of textual data. The methods reviewed are: (1) topic modeling, (2) community or group detection, and (3) cluster analysis of semantic networks. Two different datasets related to health topics were gathered from Twitter posts to compare the methods. The first dataset includes 16,138 original tweets concerning HIV pre-exposure prophylaxis (PrEP) from April 3, 2019 to April 3, 2020. The second dataset is comprised of 12,613 tweets about childhood vaccination from July 1, 2018 to October 15, 2018. Our findings suggest that the separate "topics" suggested by semantic networks (community detection) and/or cluster analysis (Ward's method) are more clearly identified than the topic modeling results. Topic modeling produced more subjects, but these tended to overlap. This study offers a better understanding of how results may vary based on method to determine subject matter chosen.

Publication Dynamics on Social Media During the Orpea Nursing Homes Scandal: A Twitter Analysis

The release of a book denouncing mistreatment in French nursing home triggered a scandal which was conveyed on social networks. The objectives of this study were to study the temporal trends and dynamics of publication on Twitter during the scandal as well as to identify the main topics of discussion.The first one is spontaneous and completely aligned with the actuality and fed by media and family of residents, while the second one is out of step with current events and fed by the company involved in the scandal.

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling

BACKGROUND

Addressing clinician documentation burden through "targeted solutions" is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees' contributions to a chat functionality-with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants' perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden.

OBJECTIVE

The objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling.

METHODS

Across the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus.

RESULTS

We uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%).

CONCLUSIONS

We conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs.

Consumer Sentiments and Emotions in New Seafood Product Concept Development: A Co-Creation Approach Using Online Discussion Rooms in Croatia, Italy and Spain

Growing Mediterranean seafood consumption, increasing consumers' awareness of food safety and quality, and changing food lifestyles are leading to the development of new food products. However, the majority of new food products launched on the market are expected to fail within the first year. One of the most effective ways to enhance new product success is by involving consumers during the first phases of New Product Development (NPD), using the so-called co-creation approach. Based on data collected through online discussion rooms, two new seafood product concepts-sardine fillets and sea burgers-were evaluated by a set of potential consumers in three Mediterranean countries-Italy, Spain, and Croatia. Textual information was analyzed by first using the topic modeling technique. Then, for each main topic identified, sentiment scores were calculated, followed by the identification of the main related emotions that were evoked. Overall, consumers seem to positively evaluate both proposed seafood product concepts, and three recurrent positive emotions (trust, anticipation, joy) were identified in relation to the main topics aroused during the discussions. The results of this study will be useful to guide future researchers and actors in this industry in the next development steps of the targeted seafood products in Mediterranean countries.

COVID-19 Vaccine-Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis

BACKGROUND

The COVID-19 vaccine is an effective tool in the fight against the COVID-19 outbreak. As the main channel of information dissemination in the context of the epidemic, social media influences public trust and acceptance of the vaccine. The rational application of health behavior theory is a guarantee of effective public health information dissemination. However, little is known about the application of health behavior theory in web-based COVID-19 vaccine messages, especially from Chinese social media posts.

OBJECTIVE

This study aimed to understand the main topics and communication characteristics of hot papers related to COVID-19 vaccine on the WeChat platform and assess the health behavior theory application with the aid of health belief model (HBM).

METHODS

A systematic search was conducted on the Chinese social media platform WeChat to identify COVID-19 vaccine-related papers. A coding scheme was established based on the HBM, and the sample was managed and coded using NVivo 12 (QSR International) to assess the application of health behavior theory. The main topics of the papers were extracted through the Latent Dirichlet Allocation algorithm. Finally, temporal analysis was used to explore trends in the evolution of themes and health belief structures in the papers.

RESULTS

A total of 757 papers were analyzed. Almost all (671/757, 89%) of the papers did not have an original logo. By topic modeling, 5 topics were identified, which were vaccine development and effectiveness (267/757, 35%), disease infection and protection (197/757, 26%), vaccine safety and adverse reactions (52/757, 7%), vaccine access (136/757, 18%), and vaccination science popularization (105/757, 14%). All papers identified at least one structure in the extended HBM, but only 29 papers included all of the structures. Descriptions of solutions to obstacles (585/757, 77%) and benefit (468/757, 62%) were the most emphasized components in all samples. Relatively few elements of susceptibility (208/757, 27%) and the least were descriptions of severity (135/757, 18%). Heat map visualization revealed the change in health belief structure before and after vaccine entry into the market.

CONCLUSIONS

To the best of our knowledge, this is the first study to assess the structural expression of health beliefs in information related to the COVID-19 vaccine on the WeChat public platform based on an HBM. The study also identified topics and communication characteristics before and after the market entry of vaccines. Our findings can inform customized education and communication strategies to promote vaccination not only in this pandemic but also in future pandemics.