scholarly journals Using Gradient Methods to Predict Twitter Users' Mental Health with Both COVID-19 Growth Patterns and Tweets

2020 ◽  
Author(s):  
Sudha Tushara Sadasivuni ◽  
Yanqing Zhang
Keyword(s):  
Mental Health ◽  
Gradient Methods ◽  
Growth Patterns ◽  
Twitter Users

Comparing Representation of Depression on Reddit and Twitter Social Media Platforms (Preprint)

2020 ◽  
Author(s):  
Ethan Kaji ◽  
Maggie Bushman
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Content Analysis ◽  
Relative Frequency ◽  
Social Activity ◽  
Social Media Platforms ◽  
Dsm Iv ◽  
Twitter Users

BACKGROUND Adolescents with depression often turn to social media to express their feelings, for support, and for educational purposes. Little is known about how Reddit, a forum-based platform, compares to Twitter, a newsfeed platform, when it comes to content surrounding depression. OBJECTIVE The purpose of this study is to identify differences between Reddit and Twitter concerning how depression is discussed and represented online. METHODS A content analysis of Reddit posts and Twitter posts, using r/depression and #depression, identified signs of depression using the DSM-IV criteria. Other youth-related topics, including School, Family, and Social Activity, and the presence of medical or promotional content were also coded for. Relative frequency of each code was then compared between platforms as well as the average DSM-IV score for each platform. RESULTS A total of 102 posts were included in this study, with 53 Reddit posts and 49 Twitter posts. Findings suggest that Reddit has more content with signs of depression with 92% than Twitter with 24%. 28.3% of Reddit posts included medical content compared to Twitter with 18.4%. 53.1% of Twitter posts had promotional content while Reddit posts didn’t contain promotional content. CONCLUSIONS Users with depression seem more willing to discuss their mental health on the subreddit r/depression than on Twitter. Twitter users also use #depression with a wider variety of topics, not all of which actually involve a case of depression.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

scholarly journals The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

2021 ◽  
Author(s):  
Senqi Zhang ◽  
Li Sun ◽  
Daiwei Zhang ◽  
Pin Li ◽  
Yue Liu ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Topic Modeling ◽  
The United States ◽  
Allocation Model ◽  
Health Concerns ◽  
Demographic Composition ◽  
The Us ◽  
Mental Health Concerns ◽  
Twitter Users

AbstractBackgroundMental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media.ObjectiveIn this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States and infer the demographic composition of Twitter users who had mental health concerns.MethodsCOVID-19 related tweets from March 5th, 2020 to January 31st, 2021 were collected through Twitter streaming API using COVID-19 related keywords (e.g., “corona”, “covid19”, “covid”). By further filtering using mental health keywords (e.g., “depress”, “failure”, “hopeless”), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users’ discussions surrounding mental health concerns. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 pandemic.ResultsWe observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that “stay-at-home”, “death poll” and “politics and policy” were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.ConclusionsThe COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males, White) were more likely to have mental health concerns during the COVID-19 pandemic.

scholarly journals Bell Let’s Talk: A Catalyst for Support and Self-Disclosure, or Corporate Greed?

2021 ◽  
Author(s):  
Harleen Dhami ◽  
Yukari Seko
Keyword(s):  
Mental Health ◽  
Research Paper ◽  
Health Needs ◽  
Mental Health Needs ◽  
Major Research ◽  
Self Disclosure ◽  
Twitter Users ◽  
Employee Mental Health

For years, the stigma around depression has caused many to suffer in silence. Since its launch in 2010, the Bell Let’s Talk campaign, started by Canadian telecommunications giant Bell, has aimed to change the narrative around mental health. With Bell coming under fire for overlooking employee mental health needs and even firing staff as a result of requesting time off, this Major Research Paper explores how the 2020 Bell Let’s Talk campaign mobilizes support and selfdisclosure among Twitter users or whether it is simply another instance of corporate profitization. Analyzing tweets one week before, the day of Bell Let’s Talk and one week after, it is suggested that the campaign does not instill a significant increase in supportive tweets on the day of. Rather, it appears that users engage in self-disclosing their experiences with depression and share resources and ways to cope on the day of the initiative. Comparatively, self-disclosure and support does not appear to be sustained beyond the day of the initiative.

scholarly journals Bell Let’s Talk: A Catalyst for Support and Self-Disclosure, or Corporate Greed?

2021 ◽  
Author(s):  
Harleen Dhami ◽  
Yukari Seko
Keyword(s):  
Mental Health ◽  
Research Paper ◽  
Health Needs ◽  
Mental Health Needs ◽  
Major Research ◽  
Self Disclosure ◽  
Twitter Users ◽  
Employee Mental Health

For years, the stigma around depression has caused many to suffer in silence. Since its launch in 2010, the Bell Let’s Talk campaign, started by Canadian telecommunications giant Bell, has aimed to change the narrative around mental health. With Bell coming under fire for overlooking employee mental health needs and even firing staff as a result of requesting time off, this Major Research Paper explores how the 2020 Bell Let’s Talk campaign mobilizes support and selfdisclosure among Twitter users or whether it is simply another instance of corporate profitization. Analyzing tweets one week before, the day of Bell Let’s Talk and one week after, it is suggested that the campaign does not instill a significant increase in supportive tweets on the day of. Rather, it appears that users engage in self-disclosing their experiences with depression and share resources and ways to cope on the day of the initiative. Comparatively, self-disclosure and support does not appear to be sustained beyond the day of the initiative.

Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media

2021 ◽  
Vol 13 (3) ◽  
pp. 1-11
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Social Media ◽  
Recurrent Neural Network ◽  
Mental Health Problems ◽  
Negative Impact ◽  
Psychological Problem ◽  
Twitter Users ◽  
British Study ◽  
F Measure

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia. The authors have adapted the recurrent neural network (RNN) in order to prevent the situations of threats, suicide, loneliness, or any other form of psychological problem through the analysis of tweets. The obtained results were validated by different experimental measures such as f-measure, recall, precision, entropy, accuracy. The RNN gives best results with 85% of accuracy compared to other techniques in literature such as social cockroaches, decision tree, and naïve Bayes.

Gender differences in social media usage related to mental health and veterans’ topics among US military veterans: An exploratory analysis (Preprint)

2020 ◽  
Author(s):  
Kacie Kelly ◽  
Alex Fine ◽  
Glen Coppersmith
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Language Processing ◽  
Help Seeking ◽  
Women Veterans ◽  
Civilian Life ◽  
Political Ideologies ◽  
Male Veterans ◽  
Twitter Users ◽  
Visualization Techniques

BACKGROUND Women veterans face particular challenges in adjusting to civilian life and in recovering from the invisible wounds of war. Because of the significant gender imbalance of the (overwhelmingly male) veteran population, and because of the particularly gendered culture of both the military and many veteran service organizations (VSOs), many women veterans are reluctant to seek medical care, counseling, and peer support from the very organizations intended to serve them. Social media has allowed new forms of community- and help-seeking, especially among individuals who are less likely to seek care for various reasons. OBJECTIVE In this article, we ask whether there is evidence that women veterans are particularly likely to use social media for the purpose of seeking community and care. This would have important implications for how VSOs provide outreach and services to this uniquely vulnerable population. METHODS We apply natural language processing (NLP) and data visualization techniques to more than 3 million Tweets collected from 20,000 Twitter users. We present a series of exploratory analyses to examine the ways women veterans use social media compared to their male counterparts. RESULTS Women veterans use social media to seek social and community engagement and to find resources and support related to mental health and veterans’ topics significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. CONCLUSIONS Our results suggest that VSOs must be systematically mindful of the gender makeup of their intended audiences when using social media for outreach or to promote services. Social media platforms may be particularly effective for reaching women veterans, and women veterans may be more likely to respond to outreach that specifically addresses the challenges veterans face. We highlight promising avenues for further research, and provide guidance concerning how VSOs can act on, test, and refine these observations in their own outreach efforts. CLINICALTRIAL n/a

scholarly journals HIV-Exposed, Uninfected Infants in Uganda Experience Poorer Growth and Body Composition Trajectories than HIV-Unexposed Infants

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1023-1023
Author(s):  
Charlotte Lane ◽  
Elizabeth Widen ◽  
Shalean Collins ◽  
Sera Young
Keyword(s):  
Mental Health ◽  
Child Health ◽  
Food Insecurity ◽  
Latent Class ◽  
Kidney Diseases ◽  
Growth Mixture Modeling ◽  
The United States ◽  
Growth Patterns ◽  
Z Scores ◽  
Hiv Exposed

Abstract Objectives To determine if HIV-exposed and -uninfected (HEU) infants whose mothers received Option B + have higher odds of experiencing suboptimal growth trajectories than HIV-unexposed uninfected (HUU) infants. Methods Anthropometric measures were taken on 238 infants (HEU = 86) at 1 week and 1,3,6,9, and 12 months. Latent class growth mixture modeling was used to develop trajectories for length-for-age z-scores, weight-for-length z-scores, MUAC, sum of skinfolds, and arm fat area. Multinomial logistic models were built to predict odds of class membership by HIV and food security status, controlling for socioeconomic factors. Results HEU infants had greater odds of being in the shortest two classes (OR = 4.30, P = 0.01 and OR = 10.70, P < 0.01) and lower odds of being in smallest arm fat area class (OR = 0.26, P = 0.01) relative to HUU infants. Food insecurity was associated with a smaller increase in the odds that HEU infants were in the second shortest class (OR interaction = 0.83, P = 0.03). Only among HEU, food insecurity increased odds of being in the lowest sum of skinfolds class (OR interaction = 3.80, P = 0.01). Conclusions HEU experience suboptimal growth patterns compared to unexposed infants. HEU infants are differentially affected by food insecurity. The mechanisms which drive these differences and successful strategies for counteracting any potential harm need further investigation. Funding Sources CL was supported by the Royster Society of Fellows. Data collection was supported by the Feed the Future Innovation Laboratory for Nutrition, which is funded by the United States Agency for International Development (USAID) and based at Tufts University (USAID OAA-L-10- 00006), and by a seed grant for collaborations between Cornell University– Ithaca and Weill Cornell Medical College faculty. EMW was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K99/R00 HD086304), the National Institute of Diabetes and Digestive and Kidney Diseases (T32DK091227 and T32DK007559), and PepsiCo Global R + D (unrestricted grant to support research in maternal and child health). SLY was supported by the National Institute of Mental Health (K01MH098902). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

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