scholarly journals Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study

10.2196/17188 ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. e17188
Author(s):  
Keren Mazuz ◽  
Elad Yom-Tov
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Large Scale ◽  
Short Form ◽  
Temporal Trends ◽  
Public Health Problem ◽  
Health Determinants ◽  
Large Scale Analysis ◽  
Interclass Correlation ◽  
Media Forum

Background Loneliness has become a public health problem described as an epidemic, and it has been argued that digital behavior such as social media posting affects loneliness. Objective The aim of this study is to expand knowledge of the determinants of loneliness by investigating online postings in a social media forum devoted to loneliness. Specifically, this study aims to analyze the temporal trends in loneliness and their associations with topics of interest, especially with those related to mental health determinants. Methods We collected a total of 19,668 postings from 11,054 users in the loneliness forum on Reddit. We asked seven crowdsourced workers to imagine themselves as writing 1 of 236 randomly chosen posts and to answer the short-form UCLA Loneliness Scale. After showing that these postings could provide an assessment of loneliness, we built a predictive model for loneliness scores based on the posts’ text and applied it to all collected postings. We then analyzed trends in loneliness postings over time and their correlations with other topics of interest related to mental health determinants. Results We found that crowdsourced workers can estimate loneliness (interclass correlation=0.19) and that predictive models are correlated with reported loneliness scores (Pearson r=0.38). Our results show that increases in loneliness are strongly associated with postings to a suicidality-related forum (hazard ratio 1.19) and to forums associated with other detrimental behaviors such as depression and illicit drug use. Clustering demonstrates that people who are lonely come from diverse demographics and from a variety of interests. Conclusions The results demonstrate that it is possible for unrelated individuals to assess people’s social media postings for loneliness. Moreover, our findings show the multidimensional nature of online loneliness and its correlated behaviors. Our study shows the advantages of studying a hard-to-reach population through social media and suggests new directions for future studies.

scholarly journals Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study (Preprint)

2019 ◽  
Author(s):  
Keren Mazuz ◽  
Elad Yom-Tov
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Large Scale ◽  
Short Form ◽  
Temporal Trends ◽  
Public Health Problem ◽  
Health Determinants ◽  
Large Scale Analysis ◽  
Interclass Correlation ◽  
Media Forum

BACKGROUND Loneliness has become a public health problem described as an epidemic, and it has been argued that digital behavior such as social media posting affects loneliness. OBJECTIVE The aim of this study is to expand knowledge of the determinants of loneliness by investigating online postings in a social media forum devoted to loneliness. Specifically, this study aims to analyze the temporal trends in loneliness and their associations with topics of interest, especially with those related to mental health determinants. METHODS We collected a total of 19,668 postings from 11,054 users in the loneliness forum on Reddit. We asked seven crowdsourced workers to imagine themselves as writing 1 of 236 randomly chosen posts and to answer the short-form UCLA Loneliness Scale. After showing that these postings could provide an assessment of loneliness, we built a predictive model for loneliness scores based on the posts’ text and applied it to all collected postings. We then analyzed trends in loneliness postings over time and their correlations with other topics of interest related to mental health determinants. RESULTS We found that crowdsourced workers can estimate loneliness (interclass correlation=0.19) and that predictive models are correlated with reported loneliness scores (Pearson <i>r</i>=0.38). Our results show that increases in loneliness are strongly associated with postings to a suicidality-related forum (hazard ratio 1.19) and to forums associated with other detrimental behaviors such as depression and illicit drug use. Clustering demonstrates that people who are lonely come from diverse demographics and from a variety of interests. CONCLUSIONS The results demonstrate that it is possible for unrelated individuals to assess people’s social media postings for loneliness. Moreover, our findings show the multidimensional nature of online loneliness and its correlated behaviors. Our study shows the advantages of studying a hard-to-reach population through social media and suggests new directions for future studies.

scholarly journals The performance of publicness in social media: tracing patterns in tweets after a disaster

2017 ◽  
Vol 40 (4) ◽  
pp. 584-599 ◽  
Author(s):  
Donald Matheson
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Public Communication ◽  
Stable Set ◽  
Computer Assisted ◽  
Social Imaginary ◽  
Media Communication ◽  
Aotearoa New Zealand ◽  
Large Scale Analysis ◽  
The Social

This article sets out to contribute to the critical understanding of public communication in social media by studying the use of Twitter after a severe earthquake in Aotearoa New Zealand in 2011. It also sets out to contribute to methodologies for studying this particular kind of publicness. It argues that the contours of the ‘social imaginary’ of the public, which are usually so hard to delineate and can be approached only in fragments or typical form, can be identified a little more clearly in the traces that people leave behind in their social media communication at critical, reflexive moments such as in the aftermath of disaster. The article draws on computer-assisted discourse analysis, specifically a corpus-linguistic-informed analysis of half a million tweets, in order to describe four main public discursive moves that were prevalent in this form of public communication. This is not to claim to describe a stable set of norms, but in fact the reverse. The article suggests that empirical, large-scale analysis of public communication in different situations, media and places opens up a project in which the varying norms of public communication are described and critiqued as they emerge in a range of discursive situations.

scholarly journals Burnout and quality of life among nurses working in selected mental health institutions in South West Nigeria

2021 ◽  
Vol 21 (3) ◽  
pp. 1428-1439
Author(s):  
Morufat A Alabi ◽  
Adeyinka G Ishola ◽  
Adenike C Onibokun ◽  
Victor O Lasebikan
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Role Conflict ◽  
Short Form ◽  
Public Health Problem ◽  
Maslach Burnout Inventory ◽  
Organizational Factors ◽  
Total Quality ◽  
Urban City

Background: Burnout remains a huge public health problem among nurses. Methods: A cross-sectional descriptive study assessed 259 nurses from two Neuropsychiatric hospitals in Nigeria. Data was collected using a sociodemographic/ job related questionnaire, the Maslach Burnout Inventory (MBI), and the Short-Form health survey (SF-12). The associations between sociodemographic characteristic and burnout was anaysed using Chi square test, between burnout and quality of life using Spearman correlation statistics. Predictors of burnout were determined using binary regression analysis Results: Prevalence of emotional exhaustion (EE) was 44.4%, depersonalization (DEP) 31.7% and reduced personal ac- complishment was 98.8%. Predictors of EE were: poor funding from management, OR = 0.38 (95% CI 0.15-0.95) and role conflict, OR = 2.44 (95% CI 1.03-5.78), while the predictors of DEP, were age group, 31-40 years, OR = 0.37 (95% CI 0.18-0.77), male gender, OR = 2.55 (95% CI 1.40-4.65), role conflict, OR = 6.53 (95% CI 0.88-7.81) and working at more urban city, OR = 3.07 (95% CI 1.54-6.16). The mean total Quality of life (QOL) scores were significantly higher among respondents who had no EE and DEP p < 0.001. Conclusion: Burnout is high among mental health nurses and is associated with poor quality of life. Keywords: Nurses; burnout; quality of life; workplace; organizational factors; more-urban.

scholarly journals Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint)

2020 ◽  
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Research Area ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Time Saving ◽  
Continuous Growth

BACKGROUND Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with <i>Lecture Notes in Computer Science</i> and <i>Journal of Medical Internet Research</i> as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

scholarly journals Twitter Data Sentiment Analysis to Understand the Effects of COVID-19 on Mental Health

2021 ◽  
Author(s):  
Adeola Adetokunbo Ayandeyi ◽  
Baidya Nath Saha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Large Scale ◽  
Research Work ◽  
Major Change ◽  
Recovery Process ◽  
Learning Tools ◽  
Sources Of Information ◽  
Medical Practitioners ◽  
The Usa

Coronavirus pandemic has caused major change in peoples’ personal and social lives. The psychological effects have been substantial because it has affected the ways people live, work, and even socialize. It has also become major discussions on social media platforms as people showcase their opinions and the effect of the virus on their mental health particularly. This pandemic is the first of its kind as humans has never encountered anything like this virus. Handling it was very difficult at first as its characteristics are peculiar. Eventually, it was detected that it is airborne and so there is need to social distance. Before the virus surfaced, some countries of the world were dealing with mental health cases, with over 40 percent of adults in the USA reported experiencing mental health challenges, including anxiety and depression. Social media has become one of the major sources of information due to information sharing on a very large scale. People perception and emotions are also portrayed through their conversations. In this research work, the interaction and conversation of people on social media, particularly Twitter, will be analyzed using machine learning tools and algorithm to determine the effect of the virus on the mental health of people and help suggest the area of concentration to medical practitioners in order to speed up the recovery process and reduce the mental health issues which has escalated due to the virus.

Towards Approximating Population-Level Mental Health in Thailand Using Large-Scale Social Media Data

2021 ◽  
pp. 334-343
Author(s):  
Krittin Chatrinan ◽  
Anon Kangpanich ◽  
Tanawin Wichit ◽  
Thanapon Noraset ◽  
Suppawong Tuarob ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Large Scale ◽  
Population Level ◽  
Social Media Data ◽  
Media Data

scholarly journals Machine Learning for Mental Health in Social Media: Bibliometric Study

10.2196/24870 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e24870
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Research Area ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Time Saving ◽  
Continuous Growth

Background Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

scholarly journals Research note: Likes, sarcasm and politics: Youth responses to a platform-initiated media literacy campaign on social media

2021 ◽  
Author(s):  
Ioana Literat ◽  
Abubakr Abdelbagi ◽  
Nicola YL Law ◽  
Marcus Y-Y Cheung ◽  
Rongwei Tang
Keyword(s):  
Social Media ◽  
Young People ◽  
Media Literacy ◽  
Large Scale ◽  
Literacy Education ◽  
Scale Analysis ◽  
Literacy Campaign ◽  
Media Literacy Education ◽  
Large Scale Analysis ◽  
Youth Attitudes

To better understand youth attitudes towards media literacy education on social media, and the opportunities and challenges inherent in such initiatives, we conducted a large-scale analysis of user responses to a recent media literacy campaign on TikTok. We found that reactions to the cam-paign were mixed, and highly political in nature. While young people appreciated the urgency of media literacy education and understood its relevance to their social media participation, many displayed a sarcastic attitude, criticizing both the content and the dissemination of the campaign. Based on these responses, we identify key takeaways and recommendations that can valuably in-form future media literacy campaigns on social media.

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