Using social media data to assess the impact of COVID-19 on mental health in China

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.

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

10.2196/preprints.29768 ◽

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

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A Sentiment Analysis and Role of Twitter for Health Communications

10.4018/978-1-7998-8421-7.ch011 ◽

2022 ◽

pp. 188-205

Author(s):

Erkan Çiçek ◽

Uğur Gündüz

Keyword(s):

Social Media ◽

Crisis Management ◽

Semantic Analysis ◽

Social Media Data ◽

Global Pandemic ◽

The Difference ◽

Textual Content ◽

The Impact ◽

Media Data

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

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Social Media to Social Media Analytics

International Journal of Technoethics ◽

10.4018/ijt.2019070104 ◽

2019 ◽

Vol 10 (2) ◽

pp. 57-70 ◽

Cited By ~ 4

Author(s):

Vikas Kumar ◽

Pooja Nanda

Keyword(s):

Social Media ◽

Social Media Analytics ◽

Social Media Data ◽

Ethical Concerns ◽

Ethical Implications ◽

The Social ◽

Social Media Platforms ◽

The Individual ◽

The Impact ◽

Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

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

10.21203/rs.3.rs-196605/v1 ◽

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.

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Social Media Reveals Psychosocial Effects of the COVID-19 Pandemic

10.1101/2020.08.07.20170548 ◽

2020 ◽

Author(s):

Koustuv Saha ◽

John Torous ◽

Eric D. Caine ◽

Munmun De Choudhury

Keyword(s):

Mental Health ◽

Social Media ◽

Psychosocial Effects ◽

Self Disclosure ◽

Social Media Data ◽

Health Concerns ◽

Mental Health Concerns ◽

Precautionary Measures ◽

Media Data ◽

Over Time

AbstractBackgroundThe novel coronavirus disease 2019 (COVID-19) pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multi-faceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a “mental health tsunami,” the psychological effects of the COVID-19 crisis remains unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.ObjectiveOur work aims to provide insights regarding people’s psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.MethodsWe obtain ∼60M Twitter streaming posts originating from the U.S. from 24 March-24 May 2020, and compare these with ∼40M posts from a comparable period in 2019 to attribute the effect of COVID-19 on people’s social media self-disclosure. Using these datasets, we study people’s self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employ transfer learning classifiers that identify the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examine the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 datasets.ResultsWe find that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis – mental health symptomatic expressions have increased by ∼14%, and support expressions have increased by ∼5%, both thematically related to COVID-19. We also observe a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlight that people express concerns that are very specific to and contextually related to the COVID-19 crisis.ConclusionsWe studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people’s mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their “new normal”. Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, healthcare and precautionary measures, and pandemic-related awareness. This work shows the potential to provide insights to mental healthcare and stakeholders and policymakers in planning and implementing measures to mitigate mental health risks amidst the health crisis.

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The impact of social media on human interaction in an organisation based on real-time social media data

International Journal of Data Science ◽

10.1504/ijds.2019.102793 ◽

2019 ◽

Vol 4 (3) ◽

pp. 260

Author(s):

Sharifah Sakinah Syed Ahmad ◽

Anis Naseerah Binti Shaik Osman ◽

Halizah Basiron

Keyword(s):

Social Media ◽

Real Time ◽

Human Interaction ◽

Social Media Data ◽

The Impact ◽

Media Data

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Studies of depression and anxiety using Reddit as a data source: Scoping review (Preprint)

10.2196/preprints.29487 ◽

2021 ◽

Author(s):

Nick Boettcher

Keyword(s):

Mental Health ◽

Social Media ◽

Scoping Review ◽

Primary Data ◽

Technical Solution ◽

Depression And Anxiety ◽

Social Media Data ◽

Primary Data Source ◽

Data Source ◽

Media Data

BACKGROUND The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the last decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies of depression and anxiety using Reddit. OBJECTIVE The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety. METHODS A scoping review was conducted using the Arksey and O’Malley framework. Academic databases searched include MEDLINE/PubMed, EMBASE, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM database. Inclusion criteria were developed using the Participants/Concept/Context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured a methodological focus on analyzing depression and/or anxiety using naturalistic written expressions from Reddit users as the primary data source. RESULTS 54 Studies were included for review. Tables and corresponding analysis delineate key methodological features including a comparatively larger focus on depression versus anxiety, an even split of original and premade datasets, a favored analytic focus on classifying the mental health states of Reddit users, and practical implications often recommending new methods of professionally-driven mental health monitoring and outreach for Reddit users. CONCLUSIONS Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology which favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications suggesting new forms of engagement with Reddit users.

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The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns

Cartography and Geographic Information Science ◽

10.1080/15230406.2014.905756 ◽

2014 ◽

Vol 42 (2) ◽

pp. 112-121 ◽

Cited By ~ 74

Author(s):

Nick Malleson ◽

Martin A. Andresen

Keyword(s):

Social Media ◽

Spatial Patterns ◽

Hot Spots ◽

Crime Rate ◽

Social Media Data ◽

The Impact ◽

Media Data

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Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010015 ◽

2018 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 4

Author(s):

Ruoxin Zhu ◽

Diao Lin ◽

Michael Jendryke ◽

Chenyu Zuo ◽

Linfang Ding ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Research Work ◽

City Management ◽

Social Media Data ◽

Social Events ◽

Time Space ◽

The Impact ◽

Media Data

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

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