Mental health-related conversations on social media and crisis episodes: a time-series regression analysis

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic

The nursing workforce is the largest discipline in healthcare and has been at the forefront of the COVID-19 pandemic response since the outbreak of COVID-19. However, the impact of COVID-19 on the nursing workforce is largely unknown as is the emotional burden experienced by nurses throughout the different waves of the pandemic. Conventional approaches often use survey question-based instruments to learn nurses' emotions, and may not reflect actual everyday emotions but the beliefs specific to survey questions. Social media has been increasingly used to express people's thoughts and feelings. This paper uses Twitter data to describe the emotional dynamics of registered nurse and student nurse groups residing in New South Wales in Australia during the COVID-19 pandemic. A novel analysis framework that considered emotions, talking topics, the unfolding development of COVID-19, as well as government public health actions and significant events was utilised to detect the emotion dynamics of nurses and student nurses. The results found that the emotional dynamics of registered and student nurses were significantly correlated with the development of COVID-19 at different waves. Both groups also showed various emotional changes parallel to the scale of pandemic waves and corresponding public health responses. The results have potential applications such as to adjust the psychological and/or physical support extended to the nursing workforce. However, this study has several limitations that will be considered in the future study such as not validated in a healthcare professional group, small sample size, and possible bias in tweets.

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult-to-treat depression

INTRODUCTION

Treatment-resistant depression (TRD) is depression unresponsive to antidepressants and affects 55% of British primary care users with depression. Current evidence is from secondary care, but long referral times mean general practitioners (GPs) manage TRD. Studies show that people with depression use Twitter to form community and document symptoms. However, Twitter remains a largely unexplored space of documented patient experience. Twitter data could provide valuable insights into learning about primary care experiences of TRD. In this study, we explored Twitter comments and conversations about TRD and produced patient-driven recommendations.

METHODS

Tweets from UK-based users were collected manually and using a browser extension in June 2021. Conventional content analysis was used to provide an overview of the Tweets, followed by interpretation to understand why Twitter may be important to people with TRD.

RESULTS

A total of 415 Tweets were organised into five clusters: self-diagnosis, symptoms, support, small wins and condition experts. These Tweets were interpreted as showing Twitter as a community for people with TRD. People had a collective sense of illness identity and were united in their experiences of TRD. However, users in the community also highlighted the absence of effective GP care, leading users to position themselves as condition experts. Users shared advice from a place of lived experience with the community but also shared potentially harmful information, including recommendations about nonevidence-based medications.

CONCLUSIONS

Findings illuminate the benefits of the TRD Twitter community and also highlight that the perception of a lack of knowledge and support from GPs may lead community members to advise nonevidenced-based medications.

PATIENT AND PUBLIC CONTRIBUTION

This study was led by a person with lived experience of TRD and bipolar. Two public contributors with mental health conditions gave feedback on our study protocol and results.

Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse

BACKGROUND

The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health.

OBJECTIVE

This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods.

METHODS

This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data.

RESULTS

The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase.

CONCLUSIONS

This study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises.

Using Twitter to Assess Stigma to Schizophrenia and Psychosis: A Qualitative Study

OBJECTIVE

The aim of the study was to evaluate stigmatizing attitudes towards schizophrenia among Turkish Twitter users.

METHODS

In the study designed with the qualitative research method, the tweets containing the keywords "schizophrenia", "schizophrenic", "psychotic" and "psychosis" in Turkish on Twitter were collected using the Knime program. The main themes and sub-themes were created by content analysis.

RESULTS

The studies revealed three major themes: "insult", "negative point of view", and "anti-stigma". While the sub-themes of "swearing" and "mocking" were determined under the main theme of "insult", the sub-theme of "false beliefs" was determined under the theme of "negative point of view", and the sub-themes of "medically appropriate" and "defensive" were determined under the main theme of "antistigma". In the results, it was determined that the word schizophrenia was commonly used to humiliate others and used as a way of addressing with slang words or to mock and that there were stigmatizing statements revealing negative feelings and thoughts in such a way that they would be inconsistent with medical information.

CONCLUSION

The results of this study can be used to develop programs to combat stigma against schizophrenia disorder and to determine content.

The window to the world for individuals with mental disorders: A qualitative study about social media

This study aimed to determine the views of individuals with mental disorders on the experience of social media. This was a qualitative study conducted using the content analysis method. Using purposive sampling, 12 individuals with mental disorders were selected and interviewed. Data were collected using semi-structured interviews and were analyzed using the content analysis method. Four main themes and 10 subthemes were identified. The themes included the window opening to the world (source of information, facilitating life), from invisibility to visibility (feeling good, liberation and socialization), negative experiences (feeling lost, envy, and privacy), and the rejection of society (escape and stigmatization). Individuals with mental disorders stated that social media had both positive as well as negative effects on their lives. The results of the study highlight the various aspects of social media use and its effects on individuals with mental disorders. These results can be used in planning and implementing mental health services for individuals with mental disorders. ACCESSIBLE SUMMARY: What is known about the subject? What does the paper add to existing knowledge? What are the implications for the practice?

Social media and schizophrenia: An update on clinical applications

Social media has redesigned the landscape of human interaction, and data obtained through these platforms are promising for schizophrenia diagnosis and management. Recent research shows mounting evidence that machine learning analysis of social media content is capable of not only differentiating schizophrenia patients from healthy controls, but also predicting conversion to psychosis and symptom exacerbations. Novel platforms such as Horyzons show promise for improving social functioning and providing timely access to therapeutic resources. Social media is also a considerable means to assess and lessen the stigma surrounding schizophrenia. Herein, the relevant literature pertaining to social media and its clinical applications in schizophrenia over the past five years are summarized, followed by a discussion centered on user feedback to highlight future directions. Social media provides valuable contributions to a multifaceted digital phenotype that may improve schizophrenia care in the near future.

Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media.

Mediterranean Diet Social Network Impact along 11 Years in the Major US Media Outlets: Thematic and Quantitative Analysis Using Twitter

Background: Media outlets influence social attitudes toward health. Thus, it is important that they share contents which promote healthy habits. The Mediterranean diet (MedDiet) is associated with lower cardiovascular disease risk. Analysis of tweets has become a tool for understanding perceptions on health issues. Methods: We investigated tweets posted between January 2009 and December 2019 by 25 major US media outlets about MedDiet and its components as well as the retweets and likes generated. In addition, we measured the sentiment analysis of these tweets and their dissemination. Results: In total, 1608 tweets, 123,363 likes and 48,946 retweets about MedDiet or its components were analyzed. Dairy (inversely weighted in MedDiet scores) accounted for 45.0% of the tweets (723/1608), followed by nuts 19.7% (317/1608). MedDiet, as an overall dietary pattern, generated only 9.8% (157/1608) of the total tweets, while olive oil generated the least number of tweets. Twitter users’ response was quantitatively related to the number of tweets posted by these US media outlets, except for tweets on olive oil and MedDiet. None of the MedDiet components analyzed was more likely to be liked or retweeted than the MedDiet itself. Conclusions: The US media outlets analyzed showed reduced interest in MedDiet as a whole, while Twitter users showed greater interest in the overall dietary pattern than in its particular components.

Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study

BACKGROUND

Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.

OBJECTIVE

This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.

METHODS

We used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.

RESULTS

Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).

CONCLUSIONS

COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

Experiences of Mental Distress during COVID-19: Thematic Analysis of Discussion Forum Posts for Anxiety, Depression, and Obsessive-Compulsive Disorder

The psychological impact of the COVID-19 pandemic on coronavirus patients, health care workers, and the general population is clear. Relatively few studies have, however, considered the impact of the pandemic on those with pre-existing mental health conditions. Therefore, the present study investigates the personal experiences of those with anxiety, depression, and obsessive-compulsive disorder during COVID-19. We conducted a qualitative study utilising Reddit discussion forum posts. We conducted three separate thematic analyses from 130 posts in subreddit forums aimed for people identifying with anxiety, depression, and obsessive-compulsive disorder. We identified a number of similar discussion forum themes (e.g., COVID-19 intensifying symptoms and a lack of social support), as well as themes that were unique to each forum type (e.g., hyperawareness and positive experiences during the pandemic). Findings should guide future practice and the support provided to those living with mental distress.

Changes in Mental Health of Women Undergoing Assisted Reproductive Technology Treatment During the COVID-19 Pandemic Outbreak in Xi'an, China

Objective: To investigate the mental health of women undergoing assisted reproductive technology (ART) treatment during the novel coronavirus pneumonia (COVID-19) pandemic outbreak in Xi'an, China. Methods: A repeated cross-sectional study was administered to women undergoing ART treatment during the outbreak period (599 women in February 2020) and the control period (892 women in May 2020) at the Northwest Women's and Children's Hospital, Xi'an, China. Results: Both the ART-treated women surveyed during the outbreak period and those surveyed during the control period had high scores on the fear dimension (0.88, 0.51). The total scores for mental health among the participants during the control period were lower than those during the outbreak period (difference = -0.22; 95% CI = -0.25, -0.18). Lower scores were also seen during the control period, compared to those in the outbreak period, for depression (difference = -0.18; 95% CI = -0.23, -0.13), neurasthenia (difference = -0.31; 95% CI = -0.36, -0.25), fear (difference = -0.37; 95% CI = -0.43, -0.31), compulsion anxiety (difference = -0.13; 95% CI = -0.16, -0.09), and hypochondriasis (difference = -0.09; 95% CI = -0.12, -0.06). Conclusions: During the COVID-19 global pandemic, the mental health of women undergoing ART treatment in Xi'an, China, was primarily manifested as fear. As the pandemic was brought under control, the mental health of ART-treated women improved. As evidenced by these results, the COVID-19 pandemic influences the mental health of women undergoing ART treatment, and clinicians should be aware of this for similar future situations.

Mental Health Discourses on Twitter during Mental Health Awareness Week

Promoting health-related campaigns on Twitter has increasingly become a world-wide choice to raise awareness and disseminate health information. Data retrieved from Twitter are now being used to explore how users express their views, attitudes and personal experiences of health-related issues. We focused on Twitter discourse reproduced during Mental Health Awareness Week 2017 by examining 1,200 tweets containing the keywords 'mental health', 'mental illness', 'mental disorders' and '#MHAW'. The analysis revealed 'awareness and advocacy', 'stigmatization', and 'personal experience of mental health/illness' as the central discourses within the sample. The article concludes with some recommendations for future research on digitally-mediated health communication.

Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech

OBJECTIVE

The growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.

DESIGN

Participants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.

SETTING

Independent living sector of a senior housing community in San Diego County.

PARTICIPANTS

Eighty English-speaking older adults with age range 66-94 (mean 83 years).

MEASUREMENTS

Interviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.

RESULTS

Lonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).

CONCLUSIONS

AI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.