Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter

Birds of a feather? Mis- and dis-information on the social media platform X related to avian influenza

Objective

Social media has become an important tool in monitoring infectious disease outbreaks such as coronavirus disease 2019 and highly pathogenic avian influenza (HPAI). Influenced by the recent announcement of a possible human death from H5N2 avian influenza, we analyzed tweets collected from X (formerly Twitter) to describe the messaging regarding the HPAI outbreak, including mis- and dis-information, concerns, and health education.

Methods

We collected tweets involving keywords relating to HPAI for 5 days (June 04 to June 08, 2024). Using topic modeling, emotion, sentiment, and user demographic analyses, we were able to describe the population and the HPAI-related topics that users discussed.

Results

With an original pool of 14,796 tweets, we analyzed a final data set of 13,319 tweets from 10,421 unique X users, with 50.4% of the tweets exhibiting negative sentiments (< 0 on a scale of -4 to +4). Predominant emotions were anger and fear shown in 36.4% and 29.5% of tweets, respectively. We identified 5 distinct, descriptive topics within the tweets. The use of emotionally charged language and spread of misinformation were substantial.

Conclusions

Mis- and dis-information about the causes of and ways to prevent HPAI infections were common. A large portion of the tweets contained references to a planned epidemic or "plandemic" to influence the upcoming 2024 US presidential election. These tweets were countered by a limited number of tweets discussing infection locations, case reports, and preventive measures. Our study can be used by public health officials and clinicians to influence the discourse on current and future outbreaks.

A pandemic of COVID-19 mis- and disinformation: manual and automatic topic analysis of the literature

Objective

Social media's arrival eased the sharing of mis- and disinformation. False information proved challenging throughout the coronavirus disease 2019 (COVID-19) pandemic with many clinicians and researchers analyzing the "infodemic." We systemically reviewed and synthesized COVID-19 mis- and disinformation literature, identifying the prevalence and content of false information and exploring mitigation and prevention strategies.

Design

We identified and analyzed publications on COVID-19-related mis- and disinformation published from March 1, 2020, to December 31, 2022, in PubMed. We performed a manual topic review of the abstracts along with automated topic modeling to organize and compare the different themes. We also conducted sentiment (ranked -3 to +3) and emotion analysis (rated as predominately happy, sad, angry, surprised, or fearful) of the abstracts.

Results

We reviewed 868 peer-reviewed scientific publications of which 639 (74%) had abstracts available for automatic topic modeling and sentiment analysis. More than a third of publications described mitigation and prevention-related issues. The mean sentiment score for the publications was 0.685, and 56% of studies had a negative sentiment (fear and sadness as the most common emotions).

Conclusions

Our comprehensive analysis reveals a significant proliferation of dis- and misinformation research during the COVID-19 pandemic. Our study illustrates the pivotal role of social media in amplifying false information. Research into the infodemic was characterized by negative sentiments. Combining manual and automated topic modeling provided a nuanced understanding of the complexities of COVID-19-related misinformation, highlighting themes such as the source and effect of misinformation, and strategies for mitigation and prevention.

Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

Twitter discussions on breastfeeding during the COVID-19 pandemic

BACKGROUND

Breastfeeding is a critical health intervention in infants. Recent literature reported that the COVID-19 pandemic resulted in significant mental health issues in pregnant and breastfeeding women due to social isolation and lack of direct professional support. These maternal mental health issues affected infant nutrition and decreased breastfeeding rates during COVID-19. Twitter, a popular social media platform, can provide insight into public perceptions and sentiment about various health-related topics. With evidence of significant mental health issues among women during the COVID-19 pandemic, the perception of infant nutrition, specifically breastfeeding, remains unknown.

METHODS

We aimed to understand public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic through Twitter analysis using natural language processing techniques. We collected and analyzed tweets related to breastfeeding and COVID-19 during the pandemic from January 2020 to May 2022. We used Python software (v3.9.0) for all data processing and analyses. We performed sentiment and emotion analysis of the tweets using natural language processing libraries and topic modeling using an unsupervised machine-learning algorithm.

RESULTS

We analyzed 40,628 tweets related to breastfeeding and COVID-19 generated by 28,216 users. Emotion analysis revealed predominantly "Positive emotions" regarding breastfeeding, comprising 72% of tweets. The overall tweet sentiment was positive, with a mean weekly sentiment of 0.25 throughout, and was affected by external events. Topic modeling revealed six significant themes related to breastfeeding and COVID-19. Passive immunity through breastfeeding after maternal vaccination had the highest mean positive sentiment score of 0.32.

CONCLUSIONS

Our study provides insight into public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic. Contrary to other topics we explored in the context of COVID (e.g., ivermectin, disinformation), we found that breastfeeding had an overall positive sentiment during the pandemic despite the documented rise in mental health challenges in pregnant and breastfeeding mothers. The wide range of topics on Twitter related to breastfeeding provides an opportunity for active engagement by the medical community and timely dissemination of advice, support, and guidance. Future studies should leverage social media analysis to gain real-time insight into public health topics of importance in child health and apply targeted interventions.

"Is a game really a reason for people to die?" Sentiment and thematic analysis of Twitter-based discourse on Indonesia soccer stampede

This study examined discourses related to an Indonesian soccer stadium stampede on 1st October 2022 using comments posted on Twitter. We conducted a lexicon-based sentiment analysis to identify the sentiments and emotions expressed in tweets and performed structural topic modeling to identify latent themes in the discourse. The majority of tweets (87.8%) expressed negative sentiments, while 8.2% and 4.0% of tweets expressed positive and neutral sentiments, respectively. The most common emotion expressed was fear (29.3%), followed by sadness and anger. Of the 19 themes identified, "Deaths and mortality" was the most prominent (15.1%), followed by "family impact". The negative stampede discourse was related to public concerns such as "vigil" and "calls for bans and suspension," while positive discourse focused more on the impact of the stampede. Public health institutions can leverage the volume and rapidity of social media to improve disaster prevention strategies.

Psychological and psychosocial determinants of COVID Health Related Behaviours (COHeRe): An evidence and gap map

Background

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. Common recommended measures to limit risk of infection include frequent handwashing, reducing the frequency of social interactions and the use of face coverings. It is important to identify those factors that can predict the uptake and maintenance of these protective behaviours.

Objectives

We aimed to identify and map the existing evidence (published and unpublished) on psychological and psychosocial factors that determine uptake and adherence to behaviours aimed at reducing the risk of infection or transmission of COVID-19.

Search Methods

Our extensive search included electronic databases (n = 12), web searches, conference proceedings, government reports, other repositories including both published peer reviewed, pre-prints and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID-19), (2) behaviours of interest and (3) terms related to psychological and psychosocial determinants of COVID Health-Related Behaviours and adherence or compliance with recommended behaviours, to capture both malleable and non-malleable determinants (i.e. determinants that could be changed and those that could not).

Selection Criteria

This Evidence and Gap Map (EGM) includes all types of studies examining determinants of common recommended behaviours aimed at mitigating human-to-human spread of COVID-19. All potential malleable and non-malleable determinants of one or more behaviours are included in the map. As part of the mapping process, categories are used to group determinants. The mapping categories were based on a previous rapid review by Hanratty 2021. These include: 'behaviour', 'cognition', 'demographics', 'disease', 'emotions', 'health status', 'information', 'intervention', and 'knowledge'. Those not suitable for categorisation in any of these groups are included in the map as 'other' determinants.

Data Collection and Analysis

Results were imported to a bibliographic reference manager where duplications of identical studies gathered from multiple sources were removed. Data extraction procedures were managed in EPPI-Reviewer software. Information on study type, population, behaviours measured and determinants measured were extracted. We appraised the methodological quality of systematic reviews with AMSTAR-2. We did not appraise the quality of primary studies in this map.

Main Results

As of 1 June 2022 the EGM includes 1034 records reporting on 860 cross-sectional, 68 longitudinal, 78 qualitative, 25 reviews, 62 interventional, and 39 other studies (e.g., mixed-methods approaches). The map includes studies that measured social distancing (n = 487), masks and face coverings (n = 382), handwashing (n = 308), physical distancing (n = 177), isolation/quarantine (n = 157), respiratory hygiene/etiquette (n = 75), cleaning surfaces (n = 59), and avoiding touching the T-zone (n = 48). There were 333 studies that assessed composite measures of two or more behaviours. The largest cluster of determinants was 'demographics' (n = 730 studies), followed by 'cognition' (n = 496 studies) and determinants categorised as 'other' (n = 447). These included factors such as 'beliefs', 'culture' and 'access to resources'. Less evidence is available for some determinants such as 'interventions' (n = 99 studies), 'information' (n = 101 studies), and 'behaviour' (149 studies).

Authors' Conclusions

This EGM provides a valuable resource for researchers, policy-makers and the public to access the available evidence on the determinants of various COVID-19 health-related behaviours. The map can also be used to help guide research commissioning, by evidence synthesis teams and evidence intermediaries to inform policy during the ongoing pandemic and potential future outbreaks of COVID-19 or other respiratory infections. Evidence included in the map will be explored further through a series of systematic reviews examining the strength of the associations between malleable determinants and the uptake and maintenance of individual protective behaviours.

Public perception of COVID-19 vaccines through analysis of Twitter content and users

BACKGROUND

With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter.

METHODS

This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period.

FINDINGS

We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time.

INTERPRETATION

This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.

[Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic]

BACKGROUND

At the beginning of the COVID‑19 pandemic in Germany, there was great uncertainty among the population and among those responsible for crisis communication. A substantial part of the communication from experts and the responsible authorities took place on social media, especially on Twitter. The positive, negative, and neutral sentiments (emotions) conveyed there during crisis communication have not yet been comparatively studied for Germany.

STUDY AIM

Sentiments in Twitter messages from various (health) authorities and independent experts on COVID‑19 will be evaluated for the first pandemic year (1 January 2020 to 15 January 2021) to provide a knowledge base for improving future crisis communication.

MATERIAL AND METHODS

From n = 39 Twitter actors (21 authorities and 18 experts), n = 8251 tweets were included in the analysis. The sentiment analysis was done using the so-called lexicon approach, a method within the social media analytics framework to detect sentiments. Descriptive statistics were calculated to determine, among other things, the average polarity of sentiments and the frequencies of positive and negative words in the three phases of the pandemic.

RESULTS AND DISCUSSION

The development of emotionality in COVID‑19 tweets and the number of new infections in Germany run roughly parallel. The analysis shows that the polarity of sentiments is negative on average for both groups of actors. Experts tweet significantly more negatively about COVID‑19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

BACKGROUND

The Supreme Court ruling in Dobbs v Jackson Women's Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information.

OBJECTIVE

This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information.

METHODS

We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest.

RESULTS

Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI -22.98% to -16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information-related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information-related terms and concomitant negative sentiment in the postdecision leak period.

CONCLUSIONS

The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

Analyzing an Emerging Pandemic on Twitter: Monkeypox

Background

Social media platforms like Twitter provide important insights into the public's perceptions of global outbreaks like monkeypox. By analyzing tweets, we aimed to identify public knowledge and opinions on the monkeypox virus and related public health issues.

Methods

We analyzed English-language tweets using the keyword "monkeypox" from 1 May to 23 July 2022. We reported gender, ethnicity, and race of Twitter users and analyzed tweets to identify predominant sentiment and emotions. We performed topic modeling and compared cohorts of users who self-identify as LGBTQ+ (an abreviation for lesbian, gay, bisexual, transgender, queer, and/or questioning) allies versus users who do not, and cohorts identified as "bots" versus humans.

Results

A total of 48 330 tweets were written by LGBTQ+ self-identified advocates or allies. The mean sentiment score for all tweets was -0.413 on a -4 to +4 scale. Negative tweets comprised 39% of tweets. The most common emotions expressed were fear and sadness. Topic modeling identified unique topics among the 4 cohorts analyzed.

Conclusions

The spread of mis- and disinformation about monkeypox was common in our tweet library. Various conspiracy theories about the origins of monkeypox, its relationship to global economic concerns, and homophobic and racial comments were common. Conversely, many other tweets helped to provide information about monkeypox vaccines, disease symptoms, and prevention methods. Discussion of rising monkeypox case numbers globally was also a large aspect of the conversation.

Conclusions

We demonstrated that Twitter is an effective means of tracking sentiment about public healthcare issues. We gained insight into a subset of people, self-identified LGBTQ+ allies, who were more affected by monkeypox.

The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis

BACKGROUND

Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms.

OBJECTIVE

The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States.

METHODS

Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the "strength of connectedness between 2 geographic areas as represented by Facebook friendship ties." This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention.

RESULTS

On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank.

CONCLUSIONS

Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression.

The Voice of the Patient and the Electronic Health Record

The patient's voice, which we define as the words the patient uses found in notes and messages and other sources, and their preferences for care and its outcomes, is too small a part of the electronic health record (EHR). To address this shortcoming will require innovation, research, funding, perhaps architectural changes to commercial EHRs, and that we address barriers that have resulted in this state, including clinician burden and financial drivers for care. Advantages to greater patient voice may accrue to many groups of EHR users and to patients themselves. For clinicians, the patient's voice, including symptoms, is invaluable in identifying new serious illness that cannot be detected by screening tests, and as an aid to accurate diagnosis. Informaticians benefit from greater patient voice in the EHR because it provides clues not found elsewhere that aid diagnostic decision support, predictive analytics, and machine learning. Patients benefit when their treatment priorities and care outcomes considered in treatment decisions. What patient voice there is in the EHR today can be found in locations not usually used by researchers. Increasing the patient voice needs be accomplished in equitable ways available to people with less access to technology and whose primary language is not well supported by EHR tools and portals. Use of direct quotations, while carrying potential for harm, permits the voice to be recorded unfiltered. If you are a researcher or innovator, collaborate with patient groups and clinicians to create new ways to capture the patient voice, and to leverage it for good.

Bubbles and lockdown in Aotearoa New Zealand: the language of self-isolation in #Covid19NZ tweets

In March 2020, as cases of COVID-19 were found in Aotearoa New Zealand, the government moved to eliminate community transmission of the virus through self-isolation. During this month, as the population discussed if, when and how households would be asked to stay at home, terms such as lockdown-the state of (national) closure-and bubble-the household isolating together-became common parts of everyday conversation.In this article, we blend quantitative and qualitative research methodologies from corpus linguistics, literary studies and the medical humanities to compare the affective range of the terms lockdown and bubble as they were used in tweets containing the hashtag #Covid19NZ. Both lockdown and bubble are metaphors of containment that provided different ways of understanding and engaging with government stay-at-home measures by highlighting and minimising different aspects of the event. We found that while the strong, prison connotations of lockdown were reflected in discussions of the measure as a tough form of control exercised from above, the lighter associations of the term bubble led to the perception of this measure as more malleable and conducive to exertion of individual control. Yet, although the seemingly restrictive range of lockdown made it a useful term for the expression of negative affect, the term was actually more frequently used with neutral or unclear affect to share information. Conversely, while bubble tweets expressed more positive sentiment, humour and support towards government stay-at-home measures, this rendered the term surprisingly restrictive in its potential uses: its lightness makes it an effective way to limit the expression of antilockdown sentiment. As Kiwi Twitter users faced the uncertainty of the first COVID-19 lockdown, the pre-existing connotations of the metaphors used to frame stay-at-home measures also helped frame their own experiences of these measures.

A global portrait of expressed mental health signals towards COVID-19 in social media space

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Sentiment Analysis on Twitter: Role of Healthcare Professionals in the Global Conversation during the AstraZeneca Vaccine Suspension

The vaccines against COVID-19 arrived in Spain at the end of 2020 along with vaccination campaigns which were not free of controversy. The debate was fueled by the adverse effects following the administration of the AstraZeneca-Oxford (AZ) vaccine in some European countries, eventually leading to its temporary suspension as a precautionary measure. In the present study, we analyze the healthcare professionals' conversations, sentiment, polarity, and intensity on social media during two periods in 2021: the one closest to the suspension of the AZ vaccine and the same time frame 30 days later. We also analyzed whether there were differences between Spain and the rest of the world. Results: The negative sentiment ratio was higher (U = 87; p = 0.048) in Spain in March (Med = 0.396), as well as the daily intensity (U = 86; p = 0.044; Med = 0.440). The opposite happened with polarity (U = 86; p = 0.044), which was higher in the rest of the world (Med = -0.264). Conclusions: There was a general increase in messages and interactions between March and April. In Spain, there was a higher incidence of negative messages and intensity compared to the rest of the world during the March period that disappeared in April. Finally, it was found that the dissemination of messages linked to negative emotions towards vaccines against COVID-19 from healthcare professionals contributed to a negative approach to primary prevention campaigns in the middle of the pandemic.

Reaching Populations at Risk for HIV Through Targeted Facebook Advertisements: Cost-Consequence Analysis

BACKGROUND

An undiagnosed HIV infection remains a public health challenge. In the digital era, social media and digital health communication have been widely used to accelerate research, improve consumer health, and facilitate public health interventions including HIV prevention.

OBJECTIVE

We aimed to evaluate and compare the projected cost and efficacy of different simulated Facebook (FB) advertisement (ad) approaches targeting at-risk populations for HIV based on new HIV diagnosis rates by age group and geographic region in the United States.

METHODS

We used the FB ad platform to simulate (without actually launching) an automatically placed video ad for a 10-day duration targeting at-risk populations for HIV. We compared the estimated total ad audience, daily reach, daily clicks, and cost. We tested ads for the age group of 13 to 24 years (in which undiagnosed HIV is most prevalent), other age groups, US geographic regions and states, and different campaign budgets. We then estimated the ad cost per new HIV diagnosis based on HIV positivity rates and the average health care industry conversion rate.

RESULTS

On April 20, 2021, the potential reach of targeted ads to at-risk populations for HIV in the United States was approximately 16 million for all age groups and 3.3 million for age group 13 to 24 years, with the highest potential reach in California, Texas, Florida, and New York. When using different FB ad budgets, the daily reach and daily clicks per US dollar followed a cumulative distribution curve of an exponential function. Using multiple US $10 ten-day ads, the cost per every new HIV diagnosis ranged from US $13.09 to US $37.82, with an average cost of US $19.45. In contrast, a 1-time national ad had a cost of US $72.76 to US $452.25 per new HIV diagnosis (mean US $166.79). The estimated cost per new HIV diagnosis ranged from US $13.96 to US $55.10 for all age groups (highest potential reach and lowest cost in the age groups 20-29 and 30-39 years) and from US $12.55 to US $24.67 for all US regions (with the highest potential reach of 6.2 million and the lowest cost per new HIV diagnosis at US $12.55 in the US South).

CONCLUSIONS

Targeted personalized FB ads are a potential means to encourage at-risk populations for HIV to be tested, especially those aged 20 to 39 years in the US South, where the disease burden and potential reach on FB are high and the ad cost per new HIV diagnosis is low. Considering the cost efficiency of ads, the combined cost of multiple low-cost ads may be more economical than a single high-cost ad, suggesting that local FB ads could be more cost-effective than a single large-budget national FB ad.

The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis

Objective

People with disabilities (PWDs) are at greater risk of COVID-19 infection, complications, and death, and experience more difficulty accessing care. We analyzed Twitter tweets to identify important topics and investigate health policies' effects on PWDs.

Methods

Twitter's application programming interface was used to access its public COVID-19 stream. English-language tweets from January 2020 to January 2022 containing a combination of keywords related to COVID-19, disability, discrimination, and inequity were collected and refined to exclude duplicates, replies, and retweets. The remaining tweets were analyzed for user demographics, content, and long-term availability.

Results

The collection yielded 94,814 tweets from 43,296 accounts. During the observation period, 1068 (2.5%) accounts were suspended and 1088 (2.5%) accounts were deleted. Account suspension and deletion among verified users tweeting about COVID-19 and disability were 0.13% and 0.3%, respectively. Emotions were similar among active, suspended, and deleted users, with general negative and positive emotions most common followed by sadness, trust, anticipation, and anger. The overall average sentiment for the tweets was negative. Ten of the 12 topics identified (96.8%) related to pandemic effects on PWDs; "politics that rejects and leaves the disabled, elderly, and children behind" (48.3%) and "efforts to support PWDs in the COVID crisis" (31.8%) were most common. The sample of tweets by organizations (43.9%) was higher for this topic than for other COVID-19-related topics the authors have investigated.

Conclusions

The primary discussion addressed how pandemic politics and policies disadvantage PWDs, older adults, and children, and secondarily expressed support for these populations. The increased level of Twitter use by organizations suggests a higher level of organization and advocacy within the disability community than in other groups. Twitter may facilitate recognition of increased harm to or discrimination against specific populations such as people living with disability during national health events.

Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic

Since early 2020, the global coronavirus pandemic has strained economic activities and traditional lifestyles. For such emergencies, our paper proposes a social sentiment estimation model that changes in response to infection conditions and state government orders. By designing mediation keywords that do not directly evoke coronavirus, it is possible to observe sentiment waveforms that vary as confirmed cases increase or decrease and as behavioral restrictions are ordered or lifted over a long period. The model demonstrates guaranteed performance with transformer-based neural network models and has been validated in New York City, Los Angeles, and Chicago, given that coronavirus infections explode in overcrowded cities. The time-series of the extracted social sentiment reflected the infection conditions of each city during the 2-year period from pre-pandemic to the new normal and shows a concurrency of waveforms common to the three cities. The methods of this paper could be applied not only to analysis of the COVID-19 pandemic but also to analyses of a wide range of emergencies and they could be a policy support tool that complements traditional surveys in the future.

A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments

The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.

A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK

BACKGROUND

In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision making.

OBJECTIVE

In this study, we aim to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the UK through a comparative geolocation and text mining analysis on Twitter.

METHODS

Over 500,000 tweets related to COVID-19 from 48 different cities in the UK were extracted, and the data cover the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and Combined Topic Modelling (CTM) for topic modelling to geospatially analyse the sentiment, emotion and topics of tweets in the UK.

RESULTS

According to the analysis, we observed a significant change in the number of tweets as the epidemiological situation and vaccination these two years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the UK. Then, the number of tweets gradually declined from February 2020. Moreover, with the identification of the COVID-19 Omicron variant in the UK in November 2021, the number of tweets grew. Our findings reveal people's attitudes and emotions towards topics related to COVID-19. For sentiment, about 60% of tweets are positive, 20% neutral and 20% are negative. For emotion, people tend to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions as the time changes towards the end of 2021. The topics are also changing during the pandemic.

CONCLUSIONS

Through large scale text mining of Twitter, our study found that there were meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts, feelings and understand their behaviours. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak epidemiological change, the government developed control measures, vaccination policies and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

The Politicization of Ivermectin Tweets During the COVID-19 Pandemic

Background

We explore the ivermectin discourse and sentiment in the United States with a special focus on political leaning through the social media blogging site Twitter.

Methods

We used sentiment analysis and topic modeling to geospatially explore ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election.

Results

All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative.

Conclusions

Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.

Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study

INTRODUCTION

The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic.

METHODS

Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained "#Scamdemic" or "#Plandemic" posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets.

RESULTS

After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was "Complaints against mandates introduced during the pandemic" (79,670 tweets), which included complaints against masks, social distancing, and closures.

DISCUSSION

While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people's lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension.

CONCLUSION

Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.

Work-from-home (WFH) during COVID-19 pandemic – A netnographic investigation using Twitter data

Purpose

This paper aims to create a better understanding of the challenges posed by work from home (WFH) during the ongoing COVID-19 pandemic, to investigate the public sentiment toward this transition, and to develop a conceptual model incorporating the relationships among the factors that influence the effectiveness of WFH.

Design/methodology/approach

This paper uses netnography method to collect data from the Twitter platform and uses Python programming language, Natural Language Processing techniques and IBM SPSS 26 to conduct sentiment analysis and directed content analysis on the data. The findings are combined with an extensive review of the remote work literature to develop a conceptual model.

Findings

Results show the majority of tweets about WFH during the pandemic are positive and objective with technology and cyber security as the most repeated topics in the tweets. New challenges to WFH during pandemic include future uncertainty, health concerns, home workspaces, self-isolation, lack of recreational activities and support mechanisms. In addition, exhaustion and technostress mediate the relationship between the antecedents and outcomes of WFH during the ongoing COVID-19 pandemic. Finally, the fear of pandemic and coping strategies moderates these relationships.

Originality/value

This paper is one of the first efforts to comprehensively investigate the challenges of WFH during a crisis and to extend the remote work literature by developing a conceptual model incorporating the moderating effects of fear of pandemic and coping strategies. Moreover, it is the first paper to investigate the tweeting behavior of different user types on Twitter who shared posts about WFH during the ongoing pandemic.

A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study

Background

Accurate and prompt clinical assessment of the severity and prognosis of patients with acute pancreatitis (AP) is critical, particularly during hospitalization. Natural language processing algorithms gain an opportunity from the growing number of free-text notes in electronic health records to mine this unstructured data, e.g., nursing notes, to detect and predict adverse outcomes. However, the predictive value of nursing notes for AP prognosis is unclear. In this study, a predictive model for in-hospital mortality in AP was developed using measured sentiment scores in nursing notes.

Methods

The data of AP patients in the retrospective cohort study were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sentiments in nursing notes were assessed by sentiment analysis. For each individual clinical note, sentiment polarity and sentiment subjectivity scores were assigned. The in-hospital mortality of AP patients was the outcome. A predictive model was built based on clinical information and sentiment scores, and its performance and clinical value were evaluated using the area under curves (AUCs) and decision-making curves, respectively.

Results

Of the 631 AP patients included, 88 cases (13.9%) cases were dead in hospital. When various confounding factors were adjusted, the mean sentiment polarity was associated with a reduced risk of in-hospital mortality in AP [odds ratio (OR): 0.448; 95% confidence interval (CI): 0.233–0.833; P=0.014]. A predictive model was established in the training group via multivariate logistic regression analysis, including 12 independent variables. In the testing group, the model showed an AUC of 0.812, which was significantly greater than the sequential organ failure assessment (SOFA) of 0.732 and the simplified acute physiology score-II (SAPS-II) of 0.792 (P<0.05). When the same level of risk was considered, the clinical benefits of the predictive model were found to be the highest compared with SOFA and SAPS-II scores.

Conclusions

The model combined sentiment scores in nursing notes showed well predictive performance and clinical value in in-hospital mortality of AP patients.

Tracking COVID-19 urban activity changes in the Middle East from nighttime lights

In response to the COVID-19 pandemic, governments around the world have enacted widespread physical distancing measures to prevent and control virus transmission. Quantitative, spatially-disaggregated information about the population-scale shifts in activity that have resulted from these measures is extremely scarce, particularly for regions outside of Europe and the US. Public health institutions often must make decisions about control measures with limited region-specific data about how they will affect societal behavior, patterns of exposure, and infection outcomes. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB), a new-generation space-borne low-light imager, has the potential to track changes in human activity, but the capability has not yet been applied to a cross-country analysis of COVID-19 responses. Here, we examine multi-year (2015–2020) daily time-series data derived from NASA’s Black Marble VIIRS nighttime lights product (VNP46A2) covering 584 urban areas, in 17 countries in the Middle East to understand how communities have adhered to COVID-19 measures in the first 4 months of the pandemic. Nighttime lights capture the onset of national curfews and lockdowns well, but also expose the inconsistent response to control measures both across and within countries. In conflict-afflicted countries, low adherence to lockdowns and curfews was observed, highlighting the compound health and security threats that fragile states face. Our findings show how satellite measurements can aid in assessing the public response to physical distancing policies and the socio-cultural factors that shape their success, especially in fragile and data-sparse regions.

Characterizing Chinese online public opinions towards the COVID-19 recovery policy

Purpose

The online users’ characteristic information can provide decision support for policy-designing and construction of public strategies. Hence, this paper aims to conduct online public opinion mining on the recovery policy stimulating the economies stroked by COVID-19 epidemic. Also, sentimental analysis is performed to uncover the posters’ emotion towards the target policy.

Design/methodology/approach

This paper adopts bidirectional encoder representations from transformers (BERT) as classifier in classification tasks, including misinformation detection, subject analysis and sentimental analysis. Meanwhile, latent Dirichlet allocation method and sentiment formulations are implemented in topic modelling and sentiment analysis.

Findings

The experimental results indicate that public opinion is mainly non-negative to the target policy. The positive emotions mainly focus on the benefits that the recovery policy might bring to stimulate economy. On the other hand, some negative opinions concerned about the shortcomings and inconvenience of the target policy.

Originality/value

The authors figured out the key factors focused by the public opinion on the target recovery policy. Also, the authors indicated pros and cons of the recovery policy by analysing the emotion and the corresponding topics of the public opinion on social media. The findings of the paper can be generalized in other countries theoretically to help them design recovery policy against COVID-19.

Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs

The COVID-19 pandemic has made transportation hubs vulnerable to public health risks. In response, policies using nonpharmaceutical interventions have been implemented, changing the way individuals interact within these facilities. However, the impact of building design and operation on policy efficacy is not fully discovered, making it critical to investigate how these policies are perceived and complied in different building spaces. Therefore, we investigate the spatial drivers of user perceptions and policy compliance in airports. Using text mining, we analyze 103,428 Google Maps reviews of 64 major hub airports in the US to identify representative topics of passenger concerns in airports (i.e., Staff, Shop, Space, and Service). Our results show that passengers express having positive experiences with Staff and Shop, but neutral or negative experiences with Service and Space, which indicates how building design has impacted policy compliance and the vulnerability of health crises. Furthermore, we discuss the actual review comments with respect to 1) spatial design and planning, 2) gate assignment and operation, 3) airport service policy, and 4) building maintenance, which will construct the foundational knowledge to improve the resilience of transportation hubs to future health crises.

The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis

BACKGROUND

The COVID-19 pandemic led to the necessity of immediate crisis communication by public health authorities. In Germany, as in many other countries, people choose social media, including Twitter, to obtain real-time information and understanding of the pandemic and its consequences. Next to authorities, experts such as virologists and science communicators were very prominent at the beginning of German Twitter COVID-19 crisis communication.

OBJECTIVE

The aim of this study was to detect similarities and differences between public authorities and individual experts in COVID-19 crisis communication on Twitter during the first year of the pandemic.

METHODS

Descriptive analysis and quantitative content analysis were carried out on 8251 original tweets posted from January 1, 2020, to January 15, 2021. COVID-19-related tweets of 21 authorities and 18 experts were categorized into structural, content, and style components. Negative binomial regressions were performed to evaluate tweet spread measured by the retweet and like counts of COVID-19-related tweets.

RESULTS

Descriptive statistics revealed that authorities and experts increasingly tweeted about COVID-19 over the period under study. Two experts and one authority were responsible for 70.26% (544,418/774,865) of all retweets, thus representing COVID-19 influencers. Altogether, COVID-19 tweets by experts reached a 7-fold higher rate of retweeting (t8,249=26.94, P<.001) and 13.9 times the like rate (t8,249=31.27, P<.001) compared with those of authorities. Tweets by authorities were much more designed than those by experts, with more structural and content components; for example, 91.99% (4997/5432) of tweets by authorities used hashtags in contrast to only 19.01% (536/2819) of experts' COVID-19 tweets. Multivariate analysis revealed that such structural elements reduce the spread of the tweets, and the incidence rate of retweets for authorities' tweets using hashtags was approximately 0.64 that of tweets without hashtags (Z=-6.92, P<.001). For experts, the effect of hashtags on retweets was insignificant (Z=1.56, P=.12).

CONCLUSIONS

Twitter data are a powerful information source and suitable for crisis communication in Germany. COVID-19 tweet activity mirrors the development of COVID-19 cases in Germany. Twitter users retweet and like communications regarding COVID-19 by experts more than those delivered by authorities. Tweets have higher coverage for both authorities and experts when they are plain and for authorities when they directly address people. For authorities, it appears that it was difficult to win recognition during COVID-19. For all stakeholders studied, the association between number of followers and number of retweets was highly significantly positive (authorities Z=28.74, P<.001; experts Z=25.99, P<.001). Updated standards might be required for successful crisis communication by authorities.

Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders-citizens of a country successful in containing the coronavirus-from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.

Among sheeples and antivaxxers: Social media responses to COVID-19 vaccine news posted by Canadian news organizations, and recommendations to counter vaccine hesitancy

Background

To create a successful public health initiative that counters vaccine hesitancy and promotes vaccine acceptance, it is essential to gain a strong understanding of the beliefs, attitudes and subjective risk perceptions of the population.

Methods

A qualitative analysis of coronavirus disease 2019 (COVID-19) vaccine discourse from 3,731 social media posts on the Twitter and Facebook accounts of six Canadian news organizations was used to identify the perceptions, attitudes, beliefs and intentions of Canadian news organizations' social media commenters toward taking a COVID-19 vaccine.

Results

Four main themes were identified: 1) COVID-19 vaccine safety and efficacy concerns; 2) conspiracy theories stemming from mistrust in government and other organizations; 3) a COVID-19 vaccine is unnecessary because the virus is not dangerous; and 4) trust in COVID-19 vaccines as a safe solution. Based on themes and subthemes, several key communication recommendations were developed for promotion of COVID-19 vaccine acceptance, including infographics championed by Public Health that highlight the benefits of the vaccine for those who have received it, public education about the contents and safety of the vaccine and eliciting an emotional connection through personal stories of those impacted by COVID-19.

Conclusion

Specific considerations, such as leveraging the public's trust in healthcare professionals to act as a liaison between Public Health and the Canadian public to communicate the benefits of the vaccine against COVID-19 and its variants, may help reduce COVID-19 vaccine hesitancy.

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

BACKGROUND

Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people's thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people's sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people's sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap.

METHODS

In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS.

RESULTS

This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people's sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted.

CONCLUSION

Various applications of opinion mining techniques in mining people's sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people's sentiments based on text in cyberspace. Kansei approach can measure people's impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence-Based Infodemiology Study

Background

Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines.

Objective

Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications.

Methods

The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy.

Results

We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events.

Conclusions

This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis

Background

The COVID-19 pandemic has led to changes in health service utilization patterns and a rapid rise in care being delivered remotely. However, there has been little published research examining patients’ experiences of accessing remote consultations since COVID-19. Such research is important as remote methods for delivering some care may be maintained in the future.

Objective

The aim of this study was to use content from Twitter to understand discourse around health and care delivery in the United Kingdom as a result of COVID-19, focusing on Twitter users’ views on and attitudes toward care being delivered remotely.

Methods

Tweets posted from the United Kingdom between January 2018 and October 2020 were extracted using the Twitter application programming interface. A total of 1408 tweets across three search terms were extracted into Excel; 161 tweets were removed following deduplication and 610 were identified as irrelevant to the research question. The remaining relevant tweets (N=637) were coded into categories using NVivo software, and assigned a positive, neutral, or negative sentiment. To examine views of remote care over time, the coded data were imported back into Excel so that each tweet was associated with both a theme and sentiment.

Results

The volume of tweets on remote care delivery increased markedly following the COVID-19 outbreak. Five main themes were identified in the tweets: access to remote care (n=267), quality of remote care (n=130), anticipation of remote care (n=39), online booking and asynchronous communication (n=85), and publicizing changes to services or care delivery (n=160). Mixed public attitudes and experiences to the changes in service delivery were found. The proportion of positive tweets regarding access to, and quality of, remote care was higher in the immediate period following the COVID-19 outbreak (March-May 2020) when compared to the time before COVID-19 onset and the time when restrictions from the first lockdown eased (June-October 2020).

Conclusions

Using Twitter data to address our research questions proved beneficial for providing rapid access to Twitter users’ attitudes to remote care delivery at a time when it would have been difficult to conduct primary research due to COVID-19. This approach allowed us to examine the discourse on remote care over a relatively long period and to explore shifting attitudes of Twitter users at a time of rapid changes in care delivery. The mixed attitudes toward remote care highlight the importance for patients to have a choice over the type of consultation that best suits their needs, and to ensure that the increased use of technology for delivering care does not become a barrier for some. The finding that overall sentiment about remote care was more positive in the early stages of the pandemic but has since declined emphasizes the need for a continued examination of people’s preference, particularly if remote appointments are likely to remain central to health care delivery.

The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses

BACKGROUND

The COVID-19 pandemic has affected populations worldwide, with extreme health, economic, social, and political implications. Health care professionals (HCPs) are at the core of pandemic response and are among the most crucial factors in maintaining coping capacities. Yet, they are also vulnerable to mental health effects caused by managing a long-lasting emergency with a lack of resources and under complicated personal concerns. However, there are a lack of longitudinal studies that investigate the HCP population.

OBJECTIVE

The aim of this study was to analyze the state of mind of HCPs as expressed in online discussions published on Twitter in light of the COVID-19 pandemic, from the onset of the pandemic until the end of 2020.

METHODS

The population for this study was selected from followers of a few hundred Twitter accounts of health care organizations and common HCP points of interest. We used active learning, a process that iteratively uses machine learning and manual data labeling, to select the large-scale population of Twitter accounts maintained by English-speaking HCPs, focusing on individuals rather than official organizations. We analyzed the topics and emotions in their discourses during 2020. The topic distributions were obtained using the latent Dirichlet allocation algorithm. We defined a measure of topic cohesion and described the most cohesive topics. The emotions expressed in tweets during 2020 were compared to those in 2019. Finally, the emotion intensities were cross-correlated with the pandemic waves to explore possible associations between the pandemic development and emotional response.

RESULTS

We analyzed the timelines of 53,063 Twitter profiles, 90% of which were maintained by individual HCPs. Professional topics accounted for 44.5% of tweets by HCPs from January 1, 2019, to December 6, 2020. Events such as the pandemic waves, US elections, or the George Floyd case affected the HCPs' discourse. The levels of joy and sadness exceeded their minimal and maximal values from 2019, respectively, 80% of the time (P=.001). Most interestingly, fear preceded the pandemic waves, in terms of the differences in confirmed cases, by 2 weeks with a Spearman correlation coefficient of ρ(47 pairs)=0.340 (P=.03).

CONCLUSIONS

Analyses of longitudinal data over the year 2020 revealed that a large fraction of HCP discourse is directly related to professional content, including the increase in the volume of discussions following the pandemic waves. The changes in emotional patterns (ie, decrease in joy and increase in sadness, fear, and disgust) during the year 2020 may indicate the utmost importance in providing emotional support for HCPs to prevent fatigue, burnout, and mental health disorders during the postpandemic period. The increase in fear 2 weeks in advance of pandemic waves indicates that HCPs are in a position, and with adequate qualifications, to anticipate pandemic development, and could serve as a bottom-up pathway for expressing morbidity and clinical situations to health agencies.

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process.

OBJECTIVE

This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center.

METHODS

We conducted an observational cross-sectional study during January 2021 at a single urban academic center. All employees who met COVID-19 allocation vaccine criteria for phase 1a.1 to 1a.4 were included. We implemented a prioritization/invitation/scheduling framework and evaluated time from invitation to scheduling as a proxy for vaccine interest and arrival to vaccine administration to measure operational throughput.

RESULTS

We allotted vaccines for 13,753 employees but only 10,662 employees with an active patient portal account received an invitation. Of those with an active account, 6,483 (61%) scheduled an appointment and 6,251 (59%) were immunized in the first 7 days. About 66% of invited providers were vaccinated in the first 7 days. In contrast, only 41% of invited facility/food service employees received the first dose of the vaccine in the first 7 days (p < 0.001). At the vaccination site, employees waited 5.6 minutes (interquartile range [IQR]: 3.9-8.3) from arrival to vaccination.

CONCLUSION

We developed a system of early COVID-19 vaccine prioritization and administration in our health care system. We saw strong early acceptance in those with proximal exposure to COVID-19 but noticed significant difference in the willingness of different employee groups to receive the vaccine.

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

The COVID-19 pandemic hit hard society, strongly affecting the emotions of the people and wellbeing. It is difficult to measure how the pandemic has affected the sentiment of the people, not to mention how people responded to the dramatic events that took place during the pandemic. This study contributes to this discussion by showing that the negative perception of the people of the COVID-19 pandemic is dropping. By negative perception, we mean the number of negative words the users of Twitter, a social media platform, employ in their online posts. Seen as aggregate, Twitter users are using less and less negative words as the pandemic evolves. The conclusion that the negative perception is dropping comes from a careful analysis we made in the contents of the COVID-19 Twitter chatter dataset, a comprehensive database accounting for more than 1 billion posts generated during the pandemic. We explore why the negativity of the people decreases, making connections with psychological traits such as psychophysical numbing, reappraisal, suppression, and resilience. In particular, we show that the negative perception decreased intensively when the vaccination campaign started in the USA, Canada, and the UK and has remained to decrease steadily since then. This finding led us to conclude that vaccination plays a key role in dropping the negativity of the people, thus promoting their psychological wellbeing.

Evaluation of a Social Media Campaign in Saskatchewan to Promote Healthy Eating During the COVID-19 Pandemic: Social Media Analysis and Qualitative Interview Study

BACKGROUND

The beginning of the COVID-19 pandemic presented many sudden challenges regarding food, including grocery shopping changes (eg, reduced store hours, capacity restrictions, and empty store shelves due to food hoarding), restaurant closures, the need to cook more at home, and closures of food access programs. Eat Well Saskatchewan (EWS) implemented a 16-week social media campaign, #eatwellcovid19, led by a dietitian and nutrition student that focused on sharing stories submitted by the Saskatchewan public about how they were eating healthy during the COVID-19 pandemic.

OBJECTIVE

The goal of this study was to describe the implementation of the #eatwellcovid19 social media campaign and the results from the evaluation of the campaign, which included campaign performance using social media metrics and experiences and perspectives of campaign followers.

METHODS

Residents of Saskatchewan, Canada, were invited to submit personal stories and experiences to EWS about how they were eating healthy during the COVID-19 pandemic from April to August 2020. Each week, one to three stories were featured on EWS social media platforms-Facebook, Instagram, and Twitter-along with evidence-based nutrition information to help residents become more resilient to challenges related to food and nutrition experienced during the COVID-19 pandemic. Individuals who submitted stories were entered into a weekly draw for a Can $100 grocery gift card. Social media metrics and semistructured qualitative interviews of campaign followers were used to evaluate the #eatwellcovid19 campaign.

RESULTS

In total, 75 stories were submitted by 74 individuals on a variety of topics (eg, grocery shopping, traditional skills, and gardening), and 42 stories were featured on social media. EWS shared 194 #eatwellcovid19 posts across social media platforms (Facebook: n=100; Instagram: n=55; and Twitter: n=39). On Facebook, #eatawellcovid19 reached 100,571 followers and left 128,818 impressions, resulting in 9575 engagements. On Instagram, the campaign reached 11,310 followers, made 14,145 impressions, and received 823 likes and 15 comments. On Twitter, #eatwellcovid19 made 15,199 impressions and received 424 engagements. Featured story submission posts had the best engagement on Facebook and the most likes and comments on Instagram. The EWS social media pages reported increases in their following during the campaign (Instagram: +30%; Facebook: +14%; and Twitter: +12%). Results from the interviews revealed that there were two types of campaign followers: those who appreciated hearing the stories submitted by followers, as it helped them to feel connected to the community during social isolation, and those who appreciated the evidence-based information.

CONCLUSIONS

Numerous stories were submitted to the #eatwellcovid19 social media campaign on various topics. On Instagram and Facebook, posts that featured these stories had the highest engagement. During this campaign, EWS's social media following increased by more than 10% on each platform. The approach used for the #eatwellcovid19 campaign could be considered by others looking to develop health promotion campaigns.

Operating room air delivery design to protect patient and surgical site results in particles released at surgical table having greater concentration along walls of the room than at the instrument tray

Background

During the coronavirus disease 2019 (COVID-19) pandemic, recommendations have included that personnel not involved in procedures releasing airborne contaminants reduce their exposure by moving >2 m away. We tested whether air particle concentrations in operating rooms (ORs) are greater in the periphery, downstream from the supply airflow.

Methods

We analyzed data from 15 mock surgical procedures performed in 3 ORs. Two ORs were modern, one with a single large diffuser system above the surgical table, and the other using a multiple diffuser array design. An air particle counting unit was located on the instrument table, another adjacent to an air return grille.

Results

Concentrations of air particles were greater at return grille than instrument table for the single large diffuser at 26 air exchanges per hour, and the multiple diffuser array at both 26 and 20 air exchanges per hour (all P ≤ .0044), including during electrocautery (all P ≤ .0072). The ratios of concentrations, return grille versus instrument table, were greater during electrocautery for 0.5 to 1.0-micron particles and 1.0 to 5.0-micron particles (both P < .0001).

Conclusions

Modern OR airflow systems are so effective at protecting the surgical field and team from airborne particles emitted during surgery that concentrations of particles released at the OR table are greater at the OR walls than near the center of the room.

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

Social media: A new tool for outbreak surveillance

Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.

What Twitter Can Tell Us About #IDWeek2020

We used topic modeling, subjectivity analysis, and social graph theory to analyze 11 944 tweets relating to IDWeek 2020. Twitter is a rich medium that can successfully disseminate knowledge and allow users to engage in social networks during a medical conference, despite a virtual format.