Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Machine learning and natural language processing to assess the emotional impact of influencers' mental health content on Instagram

Background

This study aims to examine, through artificial intelligence, specifically machine learning, the emotional impact generated by disclosures about mental health on social media. In contrast to previous research, which primarily focused on identifying psychopathologies, our study investigates the emotional response to mental health-related content on Instagram, particularly content created by influencers/celebrities. This platform, especially favored by the youth, is the stage where these influencers exert significant social impact, and where their analysis holds strong relevance. Analyzing mental health with machine learning techniques on Instagram is unprecedented, as all existing research has primarily focused on Twitter.

Methods

This research involves creating a new corpus labelled with responses to mental health posts made by influencers/celebrities on Instagram, categorized by emotions such as love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness. The study is complemented by modelling a set of machine learning algorithms to efficiently detect the emotions arising when faced with these mental health disclosures on Instagram, using the previous corpus.

Results

Results have shown that machine learning algorithms can effectively detect such emotional responses. Traditional techniques, such as Random Forest, showed decent performance with low computational loads (around 50%), while deep learning and Bidirectional Encoder Representation from Transformers (BERT) algorithms achieved very good results. In particular, the BERT models reached accuracy levels between 86-90%, and the deep learning model achieved 72% accuracy. These results are satisfactory, considering that predicting emotions, especially in social networks, is challenging due to factors such as the subjectivity of emotion interpretation, the variability of emotions between individuals, and the interpretation of emotions in different cultures and communities.

Discussion

This cross-cutting research between mental health and artificial intelligence allows us to understand the emotional impact generated by mental health content on social networks, especially content generated by influential celebrities among young people. The application of machine learning allows us to understand the emotional reactions of society to messages related to mental health, which is highly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms' high accuracy (86-90%) in social contexts like mental health, where detecting negative emotions is crucial, presents a promising research avenue. Achieving such levels of accuracy is highly valuable due to the significant implications of false positives or false negatives in this social context.

Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications

OBJECTIVES

This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions.

MATERIALS AND METHODS

We analyzed 3049 posts and comments extracted from a self-identified clinician subreddit using unsupervised machine learning via Latent Dirichlet Allocation and a structured qualitative analysis methodology.

RESULTS

Analysis uncovered 14 salient themes of ethical implications, which we further consolidated into 4 overarching domains reflecting ethical issues around various clinical applications of LLM in healthcare, LLM coding, algorithm, and data governance, LLM's role in health equity and the distribution of public health services, and the relationship between users (human) and LLM systems (machine).

DISCUSSION

Mapping themes to ethical frameworks in literature illustrated multifaceted issues covering transparent LLM decisions, fairness, privacy, access disparities, user experiences, and reliability.

CONCLUSION

This study emphasizes the need for ongoing ethical review from stakeholders to ensure responsible innovation and advocates for tailored governance to enhance LLM use in healthcare, aiming to improve clinical outcomes ethically and effectively.

Trust predicts compliance with COVID-19 containment policies: Evidence from ten countries using big data

We use Twitter, Google mobility, and Oxford policy data to study the relationship between trust and compliance over the period March 2020 to January 2021 in ten, mostly European, countries. Trust has been shown to be an important correlate of compliance with COVID-19 containment policies. However, the previous findings depend upon two assumptions: first, that compliance is time invariant, and second, that compliance can be measured using self reports or mobility measures alone. We relax these assumptions by calculating a new time-varying measure of compliance as the association between containment policies and people's mobility behavior. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. Results from various panel estimation techniques demonstrate that compliance changes over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates that compliance changes over time, and further confirms the importance of cultivating trust in others.

A Comparative Sentiment Analysis of Greek Clinical Conversations Using BERT, RoBERTa, GPT-2, and XLNet

In addressing the critical role of emotional context in patient-clinician conversations, this study conducted a comprehensive sentiment analysis using BERT, RoBERTa, GPT-2, and XLNet. Our dataset includes 185 h of Greek conversations focused on hematologic malignancies. The methodology involved data collection, data annotation, model training, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and specificity. BERT outperformed the other methods across all sentiment categories, demonstrating its effectiveness in capturing the emotional context in clinical interactions. RoBERTa showed a strong performance, particularly in identifying neutral sentiments. GPT-2 showed promising results in neutral sentiments but exhibited a lower precision and recall for negatives. XLNet showed a moderate performance, with variations across categories. Overall, our findings highlight the complexities of sentiment analysis in clinical contexts, especially in underrepresented languages like Greek. These insights highlight the potential of advanced deep-learning models in enhancing communication and patient care in healthcare settings. The integration of sentiment analysis in healthcare could provide insights into the emotional states of patients, resulting in more effective and empathetic patient support. Our study aims to address the gap and limitations of sentiment analysis in a Greek clinical context, an area where resources are scarce and its application remains underexplored.

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study

BACKGROUND

Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes.

OBJECTIVE

This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers.

METHODS

Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets.

RESULTS

Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues.

CONCLUSIONS

This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users.

"Let's get back to normal": emotions mediate the effects of persuasive messages on willingness to vaccinate for COVID-19

Objective

We examined the effectiveness of three different messages for persuading individuals to get vaccinated against COVID-19, and the role that emotions play in persuasion.

Methods

Four hundred-thirty-six participants reported their concern about the COVID-19 pandemic and confidence/hesitancy toward vaccines. Participants were randomly assigned to one of three text conditions: (1) self-interest: a persuasive message that focused on how much of a "serious threat COVID-19 is to you," and to get vaccinated to "protect yourself"; (2) self-interest + altruistic: a persuasive message that focused on the "threat to you and your community" and to get vaccinated to "protect you and your loved ones"; (3) self-interest + altruistic + normal: a persuasive message that included (2) but added "This is the only way we can get back to a normal life."; and, (4) a baseline control: no text. After reading, participants reported their emotions toward COVID-19 vaccines and their willingness to get vaccinated.

Results

Individuals in the self-interest + altruistic + normal condition were more willing to get vaccinated compared to the control condition and self-interest + altruistic condition. However, there were no differences in willingness between the self-interest + altruistic + normal condition and the self-interest condition. Moreover, emotions mediated relations between vaccine confidence/hesitancy and willingness.

Conclusion

A message that focuses on "getting back to normal" can achieve important public health action by increasing vaccine uptake to protect the population. Future work is needed across multiple countries and contexts (i.e., non-pandemic) to assess message effectiveness.

Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study

BACKGROUND

The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post-COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post-COVID-19 condition on this demographic.

OBJECTIVE

With a social media analysis of the discourse surrounding the prevalence of post-COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post-COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post-COVID-19 condition and identify critical areas and future directions for researchers and policy makers.

METHODS

From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post-COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs.

RESULTS

We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post-COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences.

CONCLUSIONS

The perceptions described on Twitter by HCWs concerning the presence of the post-COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post-COVID-19 condition.

Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing

PURPOSE

This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus.

METHODS

In this study, we collected race/ethnicity-specific data from 5 location subreddits including New York City, NY; Los Angeles, CA; Chicago, IL; Philadelphia, PA; and Houston, TX from March to December 2019 (before COVID-19 pandemic) and from March to December 2020 (during COVID-19 pandemic). Next, we applied methods from natural language processing and machine learning to analyze SDOH issues from extracted Reddit comments and conversation threads using feature engineering, topic modeling, and custom named-entity recognition (NER).

RESULTS

Topic modeling identified 35 SDOH-related topics. The SDOH-based custom NER analyses revealed that the COVID-19 pandemic significantly impacted SDOH issues of marginalized Black and Asian communities. On average, the Social and Community Context (SCC) category of SDOH had the highest percent increase (366%) from the pre-pandemic period to the pandemic period across all locations and population groups. Some of the detected SCC issues were racism, protests, arrests, immigration, police brutality, hate crime, white supremacy, and discrimination.

CONCLUSION

Reddit social media platform can be an alternative source to assess the SDOH issues of marginalized Black and Asian communities during the COVID-19 pandemic. By employing NLP/ML techniques such as LDA/GSDMM-based topic modeling and custom NER on a race/ethnicity-specific Reddit corpus, we uncovered various SDOH issues affecting marginalized Black and Asian communities that were significantly worsened during the COVID-19 pandemic. As a result of conducting this research, we recommend that researchers, healthcare providers, and governments utilize social media and collaboratively formulate responses and policies that will address SDOH issues during public health crises.

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models-BiLSTM, FFNN, and CNN - highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.

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

Introduction

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

Methods

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

Results

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

Discussion

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

The role of social media in public health crises caused by infectious disease: a scoping review

IMPORTANCE

The onset of the COVID-19 global pandemic highlighted the increasing role played by social media in the generation, dissemination and consumption of outbreak-related information.

OBJECTIVE

The objective of the current review is to identify and summarise the role of social media in public health crises caused by infectious disease, using a five-step scoping review protocol.

EVIDENCE REVIEW

Keyword lists for two categories were generated: social media and public health crisis. By combining these keywords, an advanced search of various relevant databases was performed to identify all articles of interest from 2000 to 2021, with an initial retrieval date of 13 December 2021. A total of six medical and health science, psychology, social science and communication databases were searched: PubMed, Web of Science, Scopus, Embase, PsycINFO and CNKI. A three-stage screening process against inclusion and exclusion criteria was conducted.

FINDINGS

A total of 338 studies were identified for data extraction, with the earliest study published in 2010. Thematic analysis of the role of social media revealed three broad themes: surveillance monitoring, risk communication and disease control. Within these themes, 12 subthemes were also identified. Within surveillance monitoring, the subthemes were disease detection and prediction, public attitude and attention, public sentiment and mental health. Within risk communication, the subthemes were health advice, information-seeking behaviour, infodemics/misinformation circulation, seeking help online, online distance education and telehealth. Finally, within disease control, the subthemes were government response, public behaviour change and health education information quality. It was clear that the pace of research in this area has gradually increased over time as social media has evolved, with an explosion in attention following the outbreak of COVID-19.

CONCLUSIONS AND RELEVANCE

Social media has become a hugely powerful force in public health and cannot be ignored or viewed as a minor consideration when developing public health policy. Limitations of the study are discussed, along with implications for government, health authorities and individual users. The pressing need for government and health authorities to formalise evidence-based strategies for communicating via social media is highlighted, as well as issues for individual users in assessing the quality and reliability of information consumed on social media platforms.

Exploring perceptions and experiences of stigma in Canada during the COVID-19 pandemic: a qualitative study

Background

The COVID-19 pandemic fueled stigmatization and discrimination, particularly towards individuals of Chinese or East Asian ethnicity. We conducted interviews with members of the public in Canada in order to describe and understand stigma perceptions and experiences during the COVID-19 pandemic.

Methods

We used a phenomenological approach to describe stigma experiences of Canadian residents during the COVID-19 pandemic and compared the stigma perceptions and experiences of East Asian and non-East Asian individuals. Participants were invited to take part in a single, semi-structured interview. The interview guide was rooted in the Health Stigma and Discrimination Framework (HSDF). Interviews were conducted in English, Mandarin, and Cantonese. Following participant consent, interviews were audio recorded and transcribed verbatim. Data were double coded and analyzed using qualitative content analysis guided by a framework approach.

Results

A total of 55 interviews were conducted between May and December 2020. Fifty-five percent of the sample identified as East Asian, 67.3% identified as women, and mean age was 52 years (range 20-76). Fear of infection, fear of social and economic ramifications, and blame for COVID-19 were reported drivers of stigma. Participants described preexisting perceptions on cultural norms and media influence as facilitators of stigma that propagated harmful stereotypes, particularly against Chinese and East Asian individuals. Participants observed or experienced stigmatization towards place of residence, race/ethnicity, culture, language, occupation, and age. Stigma manifestations present in the public and media had direct negative impacts on East Asian, particularly Chinese, participants, regardless of whether or not they personally experienced discrimination.

Conclusions

We used the HSDF as a rooting framework to describe perceptions and impact of stigma, particularly as they related to race/ethnicity-based stigmatization in Canada. Participants reported a number of drivers and facilitators of stigma that impacted perceptions and experiences. These findings should be used to develop sustained strategies to mitigate stigma during public health emergencies or other major crises.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44263-023-00020-7.

Curbing the COVID-19 digital infodemic: strategies and tools

A problematic manifestation of the COVID-19 pandemic is a related digital 'infodemic' with widespread dissemination of rumors, conspiracy theories, and other misinformation about the impact of the crisis on aspects of political and socio-economic life. Those spreading the misleading information did so through social media. In response, public, private and non-government stakeholders around the world have proposed a wide range of e-government policy approaches to combat this new digital phenomenon. For this Viewpoint I identified, analyzed, and classified the most interesting strategies, platforms, and tools proposed or already used by public decision-makers to combat the spread of false information related to the pandemic in a digital society.

It's the Thoughts That Count: How Psychological Distance and Affect Heuristic Influence Support for Aid Response Measures During the COVID-19 Pandemic

Guided by construal level-theory, this research seeks to understand the effect of perceived psychological distance on emotions and risk perception associated with the COVID-19 pandemic in its early stage. Survey data were collected from a nationally representative U.S. adult sample (N = 1009) in April 2020. Results reveal that social distance was negatively related to emotions and risk perception. However, hypothetical distance was not significantly related to these variables. Emotions and risk perception also mediated the relationship between social distance and support for aid response measures; theoretically, we demonstrate that people evaluate risks contingent on their emotions when making decisions. This research contributes to extant literature on psychological distance and its utility in communication messaging design during public health crises.

Analyzing User Reviews of the First Digital Contraceptive: Mixed Methods Study

BACKGROUND

People in Western countries are increasingly rejecting hormone-based birth control and expressing a preference for hormone-free methods. Digital contraceptives have emerged as nonhormonal medical devices that make use of self-tracked data and algorithms to find a user's fertile window. However, there is little knowledge about how people experience this seemingly new form of contraception, whose failure may result in unwanted pregnancies, high health risks, and life-changing consequences. As digital contraception becomes more widely adopted, examining its user experience is crucial to inform the design of technologies that not only are medically effective but also meet users' preferences and needs.

OBJECTIVE

We examined the user experience offered by Natural Cycles-the first digital contraceptive-through an analysis of app reviews written by its users worldwide.

METHODS

We conducted a mixed methods analysis of 3265 publicly available reviews written in English by users of Natural Cycles on the Google Play Store. We combined computational and human techniques, namely, topic modeling and reflexive thematic analysis.

RESULTS

For some users of digital contraception, the hormone-free aspect of the experience can be more salient than its digital aspect. Cultivating self-knowledge through the use of the technology can, in turn, feel empowering. Users also pointed to an algorithmic component that allows for increased accuracy over time as long as user diligence is applied. The interactivity of the digital contraceptive supports mutual learning and is experienced as agential and rewarding. Finally, a digital contraceptive can facilitate sharing the burden of contraceptive practices or highlight single-sided responsibilities while creating points of friction in the required daily routines.

CONCLUSIONS

Digital contraception is experienced by users as a tamed natural approach-a natural method contained and regulated by science and technology. This means that users can experience a method based on a digital product as "natural," which positions digital contraceptives as a suitable option for people looking for evidence-based nonhormonal contraceptive methods. We point to interactivity as core to the user experience and highlight that a digital contraceptive might allow for collaboration between partners around contraceptive practices and responsibilities. We note that the user diligence required for the digital contraceptive to provide accurate and frequent data is sometimes not enough. Future research could look at designing (and redesigning) digital contraceptives with primary users and intimate partners, enhancing the experience of tamed naturalness; exploring how trust fluctuates among involved actors and in interactions with the technology; and, ultimately, designing more inclusive approaches to digital contraception.

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

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

Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study

BACKGROUND

During the early pandemic, there was substantial variation in public and government responses to COVID-19 in Europe and the United States. Mass media are a vital source of health information and news, frequently disseminating this information through social media, and may influence public and policy responses to the pandemic.

OBJECTIVE

This study aims to describe the extent to which major media outlets in the United States and Spain tweeted about health-related behaviors (HRBs) relevant to COVID-19, compare the tweeting patterns between media outlets of both countries, and determine user engagement in response to these tweets.

METHODS

We investigated tweets posted by 30 major media outlets (n=17, 57% from Spain and n=13, 43% from the United States) between December 1, 2019 and May 31, 2020, which included keywords related to HRBs relevant to COVID-19. We classified tweets into 6 categories: mask-wearing, physical distancing, handwashing, quarantine or confinement, disinfecting objects, or multiple HRBs (any combination of the prior HRB categories). Additionally, we assessed the likes and retweets generated by each tweet. Poisson regression analyses compared the average predicted number of likes and retweets between the different HRB categories and between countries.

RESULTS

Of 50,415 tweets initially collected, 8552 contained content associated with an HRB relevant to COVID-19. Of these, 600 were randomly chosen for training, and 2351 tweets were randomly selected for manual content analysis. Of the 2351 COVID-19-related tweets included in the content analysis, 62.91% (1479/2351) mentioned at least one HRB. The proportion of COVID-19 tweets mentioning at least one HRB differed significantly between countries (P=.006). Quarantine or confinement was mentioned in nearly half of all the HRB tweets in both countries. In contrast, the least frequently mentioned HRBs were disinfecting objects in Spain 6.9% (56/809) and handwashing in the United States 9.1% (61/670). For tweets from the United States mentioning at least one HRB, disinfecting objects had the highest median likes and retweets, whereas mask-wearing- and handwashing-related tweets achieved the highest median number of likes in Spain. Tweets from Spain that mentioned social distancing or disinfecting objects had a significantly lower predicted count of likes compared with tweets mentioning a different HRB (P=.02 and P=.01, respectively). Tweets from the United States that mentioned quarantine or confinement or disinfecting objects had a significantly lower predicted number of likes compared with tweets mentioning a different HRB (P<.001), whereas mask- and handwashing-related tweets had a significantly greater predicted number of likes (P=.04 and P=.02, respectively).

CONCLUSIONS

The type of HRB content and engagement with media outlet tweets varied between Spain and the United States early in the pandemic. However, content related to quarantine or confinement and engagement with handwashing was relatively high in both countries.

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.

Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

BACKGROUND

Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

OBJECTIVE

This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

METHODS

We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

RESULTS

We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

CONCLUSIONS

This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

Positive and Negative Affect Schedule in early COVID-19 pandemic

The COVID-19 pandemic is the first pandemic in the Information Age. It started in Asia and spread rapidly around the world. As a consequence, millions of people were subject to lockdowns, and traditional media and social media reached more people. Our study, carried out during the lockdown, asked people about their feelings and emotions and included a Positive and Negative Affect Schedule (PANAS). Here, we present the data resulting from that study, which could potentially be reused by psychologists interested in learning about the emotional effects of the COVID-19 pandemic as well as to make comparisons before and after the lockdown period in 2020.

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States' African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g., outbreak to pandemic and coronavirus to covid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such as covid and exhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users.

Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter using deep learning

The media has been used to disseminate public information amid the Covid-19 pandemic. However, the Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to the Covid-19 news, we study user comments on the news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employ a deep-learning-based model to identify one of the 6 Ekman's basic emotions, or the absence of emotional expression, in comments to the Covid-19 news, and an implementation of Latent Dirichlet Allocation (LDA) to identify 12 different topics in the news messages. Our analysis finds that while nearly half of the user comments show no significant emotions, negative emotions are more common. Anger is the most common emotion, particularly in the media and comments about political responses and governmental actions in the United States. Joy, on the other hand, is mainly linked to media outlets from the Philippines and news on vaccination. Over time, anger is consistently the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing and occasionally spiking with news of Covid-19 variants, cases, and deaths. Emotions also vary across media outlets, with Fox News having the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness is highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Also, fear is most evident in the comments to the news from The Times of India.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

Twitterati on COVID-19 pandemic-environment linkage: Insights from mining one year of tweets

The COVID-19 pandemic seems to have had positive (although short-lived, e.g., reduction in pollution due to lockdown) as well as negative (e.g., increasing plastic pollution due to use of disposable masks, etc.) impacts on the environment. The pandemic-environment linkage also includes circumstances when regions experienced extreme weather events, such as floods and cyclones, and disaster management became challenging. This study aims to examine the trends in public discourses on Twitter on these interactions between the pandemic and environment. The present study follows the most recent literature on understanding public perceptions - which acknowledges Twitter to be an abundant source of information on public discussions on any global issue, including the pandemic. A Python-based code is developed to extract Twitter data spanning over a year, and analyze the presence of covid-environment related keywords and other attributes. It is found that the Twitterati aggressively viewed the impacts (such as economic slowdown and high mortality) of the pandemic as miniatures of the results of future climate change. The community was also highly concerned about the varying air and plastic pollution levels with the change in lockdown and covid prevention policies. Extreme weather events were a high-frequency topic when they impacted countries such as India, the USA, Australia, the Philippines and Vietnam. This study makes a novel attempt to provide an overview of public discourses on the pandemic-environment linkage and; can be a crucial addition to the literature on assessing public perception of environmental threats through Twitter data mining.

Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.

Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study

BACKGROUND

Studying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets but is limited when interpreting context. A qualitative approach allows deeper analysis of content, but is labor-intensive and feasible only for smaller data sets.

OBJECTIVE

Identify and characterize tweets containing COVID-19 misinformation.

METHODS

Tweets geolocated to the Philippines (1 January to 21 March 2020) containing the words coronavirus, covid, and ncov, were mined using GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling (BTM). Key informant interviews (KII) were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using nVivo and a combination of word frequency and text search using KII keywords, subcorpus A (n=5,881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analysis were used to further characterize these tweets. Tweets containing KII keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4,634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling.

RESULTS

BTM of the primary corpus revealed the following topics: uncertainty, lawmakers' response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and front-liners. These were categorized into four major topics: nature of COVID, contexts and consequences, people and agents of COVID, and COVID prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in these formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47) and false context (n=42). Discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), over-positivity (n=32) and marketing (n=27) . NLP identified 165 tweets with misinformation. However, manual review of these tweets showed that 115 tweets (69.7%) did not contain misinformation. .

CONCLUSIONS

An interdisciplinary approach was used to identify tweets with COVID-19 misinformation. NLP mislabeled tweets, likely due to tweets written in Filipino or a combination of Filipino and English languages. Identifying formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science, combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter. .

CLINICALTRIAL

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

BACKGROUND

Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study.

OBJECTIVE

We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies.

METHODS

We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles.

RESULTS

We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations.

CONCLUSIONS

We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

A systematic review of the use of topic models for short text social media analysis

Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-023-10471-x.

Public Support for COVID-19 Responses: Cultural Cognition, Risk Perception, and Emotions

As one of the biggest challenges facing mankind in recent history, the COVID-19 pandemic has had profound impact on the United States. However, government responses ranging from stay-at-home orders to temporary closing of nonessential businesses are not palatable for everyone. This study examines how cultural cognition, risk perception, and discrete emotions influence Americans' support for COVID-19 responses. We found that compared to communitarians and egalitarians, individualists and hierarchists were less likely to support COVID-19 responses. In addition, fear and anger mediated the relationship between risk perception and public support in the opposite direction. The highlight of this study is the moderating role of cultural cognition. Specifically, individualistic worldviews significantly moderated anger's mediation effect on the relationship between risk perception and support for COVID-19 responses.

What Do MBA Program in Southeast Asia Scholars Propose for Future COVID-19 Research in Academic Publications? A Topic Analysis Based on Autoencoder

To analyze the directions for future research suggested and to project future research plans, we extract relevant text from these publications with respect to COVID-19-related research based on 54,136 relevant academic journals published from the initial outbreak of COVID-19 in January 2020 until December 2020. First, we extract and preprocess the corpus and then determine that, according to the Elbow method, the optimal number of clusters is 7. Then, we construct a text clustering model based on an autoencoder, with the support of an artificial neural network. Distance measurements, such as correlation, cosine, Braycurtis, and Jaccard are compared, and the clustering results are evaluated with normal mutual information. The results show that cosine similarity has the best effect on clustering of COVID-19-related documents. A topic model analysis shows that the directions of future research can mainly be grouped into the following seven categories: infectivity testing, genome analysis, vaccine testing, diagnosis and infection characteristics, pandemic management, nursing care, and clinical testing. Among them, the topics of pandemic management, diagnosis and infection characteristics, and clinical testing trended upward in proportion to future directions. The topic of vaccine testing remains steady over the observation window, whereas other topics (infectivity testing, genome analysis, and nursing care) slowly trended downward. Among all the topics, medical research comprises 80%, and about 20% of the topics are related to public management, government functions, and economic development. This study enriches our scientific understanding of COVID-19 and helps us to effectively predict future scientific research output on COVID-19.

Perception of COVID-19 vaccination among Indian Twitter users: computational approach

Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding 'Co-WIN app and availability of vaccine' was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42001-023-00203-0.

Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study

BACKGROUND

For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research.

OBJECTIVE

Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data.

METHODS

This retrospective study included 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems.

RESULTS

This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive).

CONCLUSIONS

This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.

Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study

BACKGROUND

By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics.

OBJECTIVE

This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics.

METHODS

We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models.

RESULTS

We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery.

CONCLUSIONS

This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Nurses' Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions

BACKGROUND

Web-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns.

OBJECTIVE

The objective of this study was to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic.

METHODS

We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses' evolving perspectives based on temporal changes.

RESULTS

We identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns.

CONCLUSIONS

The analysis demonstrates that professional web-based forums capture nuanced details about nurses' work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises.

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

BACKGROUND

Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people's needs during a global health emergency.

OBJECTIVE

This study examined the COVID-19 pandemic-related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries.

METHODS

We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world's largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19-related chats across countries.

RESULTS

Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: "Questions on COVID-19 asked to the chatbot" (30.6%), "Preventive behaviors" (25.3%), "Outbreak of COVID-19" (16.4%), "Physical and psychological impact of COVID-19" (16.0%), and "People and life in the pandemic" (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19.

CONCLUSIONS

Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people's informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.

Understanding Digital Mental Health Needs and Usage With an Artificial Intelligence-Led Mental Health App (Wysa) During the COVID-19 Pandemic: Retrospective Analysis

BACKGROUND

There has been a surge in mental health concerns during the COVID-19 pandemic, which has prompted the increased use of digital platforms. However, there is little known about the mental health needs and behaviors of the global population during the pandemic. This study aims to fill this knowledge gap through the analysis of real-world data collected from users of a digital mental health app (Wysa) regarding their engagement patterns and behaviors, as shown by their usage of the service.

OBJECTIVE

This study aims to (1) examine the relationship between mental health distress, digital health uptake, and COVID-19 case numbers; (2) evaluate engagement patterns with the app during the study period; and (3) examine the efficacy of the app in improving mental health outcomes for its users during the pandemic.

METHODS

This study used a retrospective observational design. During the COVID-19 pandemic, the app's installations and emotional utterances were measured from March 2020 to October 2021 for the United Kingdom, the United States of America, and India and were mapped against COVID-19 case numbers and their peaks. The engagement of the users from this period (N=4541) with the Wysa app was compared to that of equivalent samples of users from a pre-COVID-19 period (1000 iterations). The efficacy was assessed for users who completed pre-post assessments for symptoms of depression (n=2061) and anxiety (n=1995) on the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) test measures, respectively.

RESULTS

Our findings demonstrate a significant positive correlation between the increase in the number of installs of the Wysa mental health app and the peaks of COVID-19 case numbers in the United Kingdom (P=.02) and India (P<.001). Findings indicate that users (N=4541) during the COVID period had a significantly higher engagement than the samples from the pre-COVID period, with a medium to large effect size for 80% of these 1000 iterative samples, as observed on the Mann-Whitney test. The PHQ-9 and GAD-7 pre-post assessments indicated statistically significant improvement with a medium effect size (PHQ-9: P=.57; GAD-7: P=.56).

CONCLUSIONS

This study demonstrates that emotional distress increased substantially during the pandemic, prompting the increased uptake of an artificial intelligence-led mental health app (Wysa), and also offers evidence that the Wysa app could support its users and its usage could result in a significant reduction in symptoms of anxiety and depression. This study also highlights the importance of contextualizing interventions and suggests that digital health interventions can provide large populations with scalable and evidence-based support for mental health care.

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.

An analytics approach to health and healthcare in citizen science communications on Twitter

Background

Citizen science is a growing practice in which volunteers, including non-scientists, conduct or contribute to research by collecting and analyzing data. The increasing importance of citizen science in the last years has led to an increased interest in detecting how citizen science can contribute to scientific advancements in different areas. Recent research shows that citizen science has become a means of engagement between scientist and the public, encouraging scientific curiosity and promoting scientific knowledge.

Methods

In this article, we report on how to apply computational analysis techniques to Twitter messages to reveal the impact of citizen science in health-related areas. The main objectives are (1) to characterize central topics of these discussions, and (2) to identify particularly important actors in these social media networks.

Results

For the topics, our findings suggest that sustainable development goals, technologies and health, and COVID-19 are those most addressed by the users. Other topics represented in the data are cancer, public health, mental health, and health and well being of sea and earth living creatures related to sustainable development goals.

Conclusion

Based on our results, those entities or actors who are most cited and retweeted are Twitter accounts of projects and not primarily individual professionals or citizen scientists.

A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

The COVID-19 Response in North America

In our Information Technology (IT) based societies, social media plays an important role in communications and social networks for COVID-19. This study explores social responses for COVID-19 in North America, which is the most severe continent affected by the COVID-19 pandemic. This study employs social network analysis for Twitter among the US, Canada, and Mexico. This study finds that the 3 countries show different characteristics of social networks for COVID-19. For example, the Prime Minister plays the second most important role in the Canadian networks, whereas the Presidents play the most significant role in them, in the US, and Mexico. WHO shows a pivotal effect on social networks of COVID-19 in Canada and the US, whereas it does not affect them in Mexico. Canadians are interested in COVID-19 apps, the American people criticize the president and administration as incompetent in terms of COVID-19, and the Mexican people search for COVID-19 cases and the pandemic in Mexico. This study shows that governments and disease experts should understand social networks and communications of social network services, to develop effective COVID-19 policies according to the characteristics of their country.

Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R²-score of 0.929.

Flattening the COVID-19 curve: Emotions mediate the effects of a persuasive message on preventive action

Introduction

Across four countries (Canada, USA, UK, and Italy), we explored the effects of persuasive messages on intended and actual preventive actions related to COVID-19, and the role of emotions as a potential mechanism for explaining these effects.

Methods

One thousand seventy-eight participants first reported their level of concern and emotions about COVID-19 and then received a positive persuasive text, negative persuasive text, or no text. After reading, participants reported their emotions about the pandemic and their willingness to take preventive action. One week following, the same participants reported the frequency with which they engaged in preventive action and behaviors that increased the risk of contracting COVID-19.

Results

Results revealed that the positive persuasive text significantly increased individuals' willingness to and actual engagement in preventive action and reduced risky behaviors 1 week following the intervention compared to the control condition. Moreover, significant differences were found between the positive persuasive text condition and negative persuasive text condition whereby individuals who read the positive text were more willing and actually engaged in more preventive action compared to those who read the negative text. No differences were found, however, at the 1-week follow-up for social distancing and isolation behaviors. Results also revealed that specific discrete emotions mediated relations between the effects of the texts and preventive action (both willing and actual).

Discussion

This research highlights the power of educational interventions to prompt behavioral change and has implications for pandemic-related interventions, government policy on health promotion messages, and future research.

Improving Public Health Policy by Comparing the Public Response during the Start of COVID-19 and Monkeypox on Twitter in Germany: A Mixed Methods Study

Little is known about monkeypox public concerns since its widespread emergence in many countries. Tweets in Germany were examined in the first three months of COVID-19 and monkeypox to examine concerns and issues raised by the public. Understanding views and positions of the public could help to shape future public health campaigns. Few qualitative studies reviewed large datasets, and the results provide the first instance of the public thinking comparing COVID-19 and monkeypox. We retrieved 15,936 tweets from Germany using query words related to both epidemics in the first three months of each one. A sequential explanatory mixed methods research joined a machine learning approach with thematic analysis using a novel rapid tweet analysis protocol. In COVID-19 tweets, there was the selfing construct or feeling part of the emerging narrative of the spread and response. In contrast, during monkeypox, the public considered othering after the fatigue of the COVID-19 response, or an impersonal feeling toward the disease. During monkeypox, coherence and reconceptualization of new and competing information produced a customer rather than a consumer/producer model. Public healthcare policy should reconsider a one-size-fits-all model during information campaigns and produce a strategic approach embedded within a customer model to educate the public about preventative measures and updates. A multidisciplinary approach could prevent and minimize mis/disinformation.

Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students' mental health

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to "COVID-19", "suicide", or "self-harm". An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments' responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.