Assessing the mental health of college students by leveraging social media data

2021 ◽  
Vol 28 (1) ◽  
pp. 54-58
Author(s):  
Koustuv Saha ◽  
Munmun De Choudhury
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Media ◽  
Young Adults ◽  
Real Time ◽  
Health Needs ◽  
Mental Health Needs ◽  
Social Media Data ◽  
Use Of Social Media ◽  
Media Data

The mental health of college students is a growing concern and gauging the mental health needs of this group is difficult to assess in real-time and in scale. The ubiquity and widespread use of social media, particularly among young adults, provides opportunities for various stakeholders to proactively assess the mental health of college students and provide timely and tailored support.

scholarly journals Mental Health Consultations on College Campuses: Examining the Predictive Ability of Social Media

2021 ◽  
Author(s):  
Koustuv Saha ◽  
Asra Yousuf ◽  
Ryan L. Boyd ◽  
James W. Pennebaker ◽  
Munmun Choudhury
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Media ◽  
Collective Identity ◽  
Ground Truth ◽  
Treatment Needs ◽  
Ground Truth Data ◽  
Social Media Data ◽  
Mental Health Consultations ◽  
Media Data

Abstract The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable “passive sensor” of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs.

scholarly journals Social Media Discussions Predict Mental Health Consultations on College Campuses

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Koustuv Saha ◽  
Asra Yousuf ◽  
Ryan L. Boyd ◽  
James W. Pennebaker ◽  
Munmun De Choudhury
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Media ◽  
Ground Truth ◽  
Treatment Needs ◽  
Ground Truth Data ◽  
Social Media Data ◽  
Passive Sensor ◽  
Mental Health Consultations ◽  
Media Data

AbstractThe mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs.

scholarly journals Understanding the Role of Social Media–Based Mental Health Support Among College Students: Survey and Semistructured Interviews (Preprint)

2020 ◽  
Author(s):  
Piper Vornholt ◽  
Munmun De Choudhury
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Media ◽  
Mental Illness ◽  
Health Needs ◽  
Mental Health Needs ◽  
Semistructured Interviews ◽  
Social Media Platforms ◽  
Mental Health Support ◽  
Health Support

BACKGROUND Mental illness is a growing concern within many college campuses. Limited access to therapy resources, along with the fear of stigma, often prevents students from seeking help. Introducing supportive interventions, coping strategies, and mitigation programs might decrease the negative effects of mental illness among college students. OBJECTIVE Many college students find social support for a variety of needs through social media platforms. With the pervasive adoption of social media sites in college populations, in this study, we examine whether and how these platforms may help meet college students’ mental health needs. METHODS We first conducted a survey among 101 students, followed by semistructured interviews (n=11), of a large public university in the southeast region of the United States to understand whether, to what extent, and how students appropriate social media platforms to suit their struggle with mental health concerns. The interviews were intended to provide comprehensive information on students’ attitudes and their perceived benefits and limitations of social media as platforms for mental health support. RESULTS Our survey revealed that a large number of participating students (71/101, 70.3%) had recently experienced some form of stress, anxiety, or other mental health challenges related to college life. Half of them (52/101, 51.5%) also reported having appropriated some social media platforms for self-disclosure or help, indicating the pervasiveness of this practice. Through our interviews, we obtained deeper insights into these initial observations. We identified specific academic, personal, and social life stressors; motivations behind social media use for mental health needs; and specific platform affordances that helped or hindered this use. CONCLUSIONS Students recognized the benefits of social media in helping connect with peers on campus and promoting informal and candid disclosures. However, they argued against complete anonymity in platforms for mental health help and advocated the need for privacy and boundary regulation mechanisms in social media platforms supporting this use. Our findings bear implications for informing campus counseling efforts and in designing social media–based mental health support tools for college students.

scholarly journals Mental Health Consultations on College Campuses: Examining the Predictive Ability of Social Media

2021 ◽  
Author(s):  
Koustuv Saha ◽  
Asra Yousuf ◽  
Ryan Boyd ◽  
James Pennebaker ◽  
Munmun De Choudhury
Keyword(s):  
Mental Health ◽  
College Students ◽  
Social Media ◽  
Collective Identity ◽  
Ground Truth ◽  
Treatment Needs ◽  
Ground Truth Data ◽  
Social Media Data ◽  
Mental Health Consultations ◽  
Media Data

Abstract The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable "passive sensor" of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.

scholarly journals Mining Social Media Data for Biomedical Signals and Health-Related Behavior

2020 ◽  
Vol 3 (1) ◽  
pp. 433-458 ◽  
Author(s):  
Rion Brattig Correia ◽  
Ian B. Wood ◽  
Johan Bollen ◽  
Luis M. Rocha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Population Level ◽  
Data Access ◽  
Health Conditions ◽  
Social Phenomena ◽  
Medical Treatments ◽  
Social Media Data ◽  
Health Related ◽  
Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

scholarly journals Anatomy of a Protest: Spatial Information, Social Media, and Urban Space

2020 ◽  
Vol 6 (1) ◽  
pp. 205630511989732
Author(s):  
Alireza Karduni ◽  
Eric Sauda
Keyword(s):  
Social Media ◽  
Public Space ◽  
Urban Space ◽  
Language Processing ◽  
Local Community ◽  
Spatial Information ◽  
Social Media Data ◽  
Public Events ◽  
Use Of Social Media ◽  
Media Data

Black Lives Matter, like many modern movements in the age of information, makes significant use of social media as well as public space to demand justice. In this article, we study the protests in response to the shooting of Keith Lamont Scott by police in Charlotte, North Carolina, on September 2016. Our goal is to measure the significance of urban space within the virtual and physical network of protesters. Using a mixed-methods approach, we identify and study urban space and social media generated by these protests. We conducted interviews with protesters who were among the first to join the Keith Lamont Scott shooting demonstrations. From the interviews, we identify places that were significant in our interviewees’ narratives. Using a combination of natural language processing and social network analysis, we analyze social media data related to the Charlotte protests retrieved from Twitter. We found that social media, local community, and public space work together to organize and motivate protests and that public events such as protests cause a discernible increase in social media activity. Finally, we find that there are two distinct communities who engage social media in different ways; one group involved with social media, local community and urban space, and a second group connected almost exclusively through social media.

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