Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence

Self-Organizing Maps for Secondary Ion Mass Spectrometry

Secondary ion mass spectrometry (SIMS) is a powerful analytical technique for characterizing the molecular and elemental composition of surfaces. Individual mass spectra can provide information about the mean surface composition, while spatial mapping can elucidate the spatial distributions of molecular species in 2D and 3D with no prior labeling of molecular targets. The data sets produced by SIMS techniques are large and inherently complex, often containing subtle relationships between spatial and molecular features. Machine learning algorithms are well suited to exploring this complexity, making them ideal for data analysis, interpretation, and visualization of SIMS data sets. One such algorithm, the self-organizing map (SOM), is particularly well suited to clustering similar samples and reducing the dimensionality of hyperspectral data sets. Here, we present an introduction to the SOM, a concise mathematical description, and recent examples of its use in SIMS and other related mass spectrometry techniques. These examples demonstrate how SOMs may be used to interpret high volumes of individual mass spectra, imaging, or depth profiling data sets. This review will be useful for specialists in SIMS and other mass spectral techniques seeking to explore self-organizing maps for data analysis.

ChatGPT and generative AI in urology and surgery-A narrative review

Introduction

ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery.

Methods

We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms 'ChatGPT' and 'surgery' were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed.

Results

Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality.

Conclusion

The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.

Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube

This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination. While viewers engaged most with videos about infection origins, treatment, and vaccination, fear and sadness in the text consistently drove views, likes, and comments. Visuals primarily conveyed happiness and sadness, but their influence on engagement varied. This research highlights the crucial role YouTube plays in disseminating COVID-19 patient narratives and suggests its potential for improving health communication strategies. By understanding how emotions and content influence viewer engagement, healthcare professionals and public health officials can tailor their messaging to better connect with the public and address pandemic-related anxieties.

Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings

OBJECTIVES

Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language.

MATERIALS AND METHODS

Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient-nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 "utterances" that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels.

RESULTS

A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients' tendency to use informal language and keywords related to "religion," "home," and "money," while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions.

CONCLUSION

The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.

(Im)mobility and performance of emotions: Chinese international students' difficult journeys to home during the COVID-19 pandemic

This article examines mediated performances of emotions by Chinese international students in their transnational journeys returning to China during the COVID-19 pandemic with a focus on the role of mobile media in helping students cope with their cross-border (im)mobility and symbolic immobility. By thematically analyzing 36 self-representational videos produced by returning Chinese students on a burgeoning mobile media platform Douyin, we identify 5 overarching themes of emotional performance: fear, pride, gratitude, shame, and solidarity. We propose that mobile media has the potential to create a hybrid space that witnesses and elicits empathy for the hardship experienced by marginalized mobile groups during the global pandemic. Mobile media, by enabling simultaneous communication, amplifies the sensation of belonging in times of isolation and ambiguity and offers dialogic venues for disparate groups across geographical and socioemotional distances. Our findings suggest the vulnerability of mobile communities in the event of a global pandemic, and the affordances of mobile media in confronting and resolving such precarity. We call attention to the intersections of mobile communities and mobile media amid the global pandemic, particularlyon the experiences and performances of emotions in hybrid spaces.

Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

BACKGROUND

Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France.

OBJECTIVE

This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.

METHODS

This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets.

RESULTS

A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies.

CONCLUSIONS

Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Long COVID in the context of social determinants of health

The COVID-19 pandemic has been a challenge for the public health system and has highlighted health disparities. COVID-19 vaccines have effectively protected against infection and severe disease, but some patients continue to suffer from symptoms after their condition is resolved. These post-acute sequelae, or long COVID, continues to disproportionately affect some patients based on their social determinants of health (SDOH). This paper uses the World Health Organization's (WHO) SDOH conceptual framework to explore how SDOH influences long COVID outcomes.

Targeting COVID-19 vaccine-hesitancy in college students: An audience-centered approach

Objective: The study tested potential factors that differentiated the COVID-19 vaccine-hesitant and -inclined college students and, based on these factors, identified subgroups of the vaccine-hesitant students. Participants: Participants were 1,183 U.S. college students attending four-year universities or community colleges recruited through Qualtrics between January 25 and March 3, 2021. Methods: Participants completed an online survey assessing their COVID-19 vaccination intention, perceived risks of COVID-19 and the COVID-19 vaccines, efficacy beliefs regarding COVID-19 and the COVID-19 vaccines, and emotions toward taking the COVID-19 vaccines. Results: Vaccine-hesitant and -inclined college students varied in their emotions, risk perceptions, and efficacy beliefs regarding the virus and the vaccines. Using these factors as indicators, vaccine-hesitant college students were classified into five latent subgroups with distinct characteristics. Conclusions: In identifying subgroups of the vaccine-hesitant college students, the study has important insights to offer regarding the design of vaccine-promotion messaging strategies targeting the college student population.

Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach

Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.

Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis

OBJECTIVES

The objective of this study is to investigate, from a longitudinal perspective, how WHO communicated COVID-19 related information to the public through its press conferences during the first two years of the pandemic.

METHODS

The transcripts of 195 WHO COVID-19 press conferences held between January 22, 2020 and February 23, 2022 were collected. All transcripts were syntactically parsed to extract highly frequent noun chunks that were potential topics of the press conferences. First-order autoregression models were fit to identify "hot" and "cold" topics. In addition, sentiments and emotions expressed in the transcripts were analyzed using lexicon-based sentiment/emotion analyses. Mann-Kendall tests were performed to capture the possible trends of sentiments and emotions over time.

RESULTS

First, eleven "hot" topics were identified. These topics were pertinent to anti-pandemic measures, disease surveillance and development, and vaccine-related issues. Second, no significant trend was captured in sentiments. Last, significant downward trends were found in anticipation, surprise, anger, disgust, and fear. However, no significant trends were found in joy, trust, and sadness.

CONCLUSIONS

This retrospective study provided new empirical evidence on how WHO communicated issues pertaining to COVID-19 to the general public through its press conferences. With the help of the study, members of the general public, health organizations, and other stake-holders will be able to better understand the way in which WHO has responded to various critical events during the first two years of the pandemic.

Social isolation and the brain: effects and mechanisms

An obvious consequence of the coronavirus disease (COVID-19) pandemic is the worldwide reduction in social interaction, which is associated with many adverse effects on health in humans from babies to adults. Although social development under normal or isolated environments has been studied since the 1940s, the mechanism underlying social isolation (SI)-induced brain dysfunction remains poorly understood, possibly due to the complexity of SI in humans and translational gaps in findings from animal models. Herein, we present a systematic review that focused on brain changes at the molecular, cellular, structural and functional levels induced by SI at different ages and in different animal models. SI studies in humans and animal models revealed common socioemotional and cognitive deficits caused by SI in early life and an increased occurrence of depression and anxiety induced by SI during later stages of life. Altered neurotransmission and neural circuitry as well as abnormal development and function of glial cells in specific brain regions may contribute to the abnormal emotions and behaviors induced by SI. We highlight distinct alterations in oligodendrocyte progenitor cell differentiation and oligodendrocyte maturation caused by SI in early life and later stages of life, respectively, which may affect neural circuit formation and function and result in diverse brain dysfunctions. To further bridge animal and human SI studies, we propose alternative animal models with brain structures and complex social behaviors similar to those of humans.

The utility of the emBODY tool as a novel method of studying complex phenomena-related emotions

Bodily sensations are one of the major building blocks of emotional experience. However, people differ in their ability to recognise and name their emotions, especially those in response to complex phenomena such as climate change or the COVID-19 pandemic. Therefore, we investigated whether the bodily sensation maps (BSMs) approach can be employed to study emotions related to phenomena that are likely to evoke various, and perhaps even conflicting, emotions in people. Using a unique topographical self-report method-the previously established emBODY tool, 548 participants marked where in the body they feel sensations (activations and deactivations) when they experience distinct emotions (e.g. happiness) and when they think about different phenomena, namely climate change, COVID-19 pandemic, war, nature, friends, and summer holidays. We revealed maps of bodily sensations associated with different emotions and phenomena. Importantly, each phenomenon was related to a statistically unique BSM, suggesting that participants were able to differentiate between feelings associated with distinct phenomena. Yet, we also found that BSMs of phenomena showed some similarity with maps of emotions. Together, these findings indicate that the emBODY tool might be useful in uncovering the range of emotions individuals experience towards complex phenomena.

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia

Background

Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.

Objective

The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.

Methods

We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.

Results

Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.

Conclusions

This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

An artificial intelligence life cycle: From conception to production

This paper presents the "CDAC AI life cycle," a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases-design, develop, and deploy-and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.

Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population's mental health and health programs. It is complex for public organizations to measure the population's mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population's mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter's usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study

Online mental health service (OMHS) platforms have contributed significantly to the public’s mental health during the COVID-19 pandemic in China. However, it remains unclear why the public used OMHS platforms for psychological help-seeking (PHS) behavior and how PHS behavior varied across different stages of the COVID-19 pandemic. Based on the ecological PHS behavior data from two OMHS platforms, we extracted population, psychological problems, and influential factors of PHS behavior by text mining and time series analysis methods. Seven top-ranked psychological problems (i.e., depression and anxiety, lack of interest, suicidal tendencies, social phobia, feelings of being worried and afraid, suffering, anger) and seven influential factors (i.e., interpersonal relationships, love, family, work, psychotherapy, personal characteristics, marriage) were found. The online PHS behaviors related to different psychological problems and influential factors remained a growing trend before 2020 and have been increasing significantly due to the COVID-19 outbreak. Four main stages were found during the pandemic according to the changes in the online PHS population: sharp growth, significant decline, slight rebound, and slow decline. This study identified large-scale, spontaneous PHS behaviors among the online public during the COVID-19 pandemic and the various psychological problems and influential factors that varied across different stages of the pandemic, suggesting that the government and health practitioners should adopt effective policies and strategies to prevent and intervene in mental health problems for the online public.