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 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 Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study (Preprint)

2019 ◽  
Author(s):  
Derek Howard ◽  
Marta M Maslej ◽  
Justin Lee ◽  
Jacob Ritchie ◽  
Geoffrey Woollard ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Transfer Learning ◽  
Computational Linguistics ◽  
Feature Representation ◽  
Fine Tuning ◽  
Language Models ◽  
Universal Sentence ◽  
Text Feature

BACKGROUND Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.

scholarly journals Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5924
Author(s):  
Yi Ji Bae ◽  
Midan Shim ◽  
Won Hee Lee
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Negative Emotion ◽  
Supervised Machine Learning ◽  
Control Group ◽  
Learning Approaches ◽  
Linguistic Features ◽  
Media Texts

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

Social Media Content Analysis

2021 ◽  
pp. 156-174
Author(s):  
D. Sudaroli Vijayakumar ◽  
Senbagavalli M. ◽  
Jesudas Thangaraju ◽  
Sathiyamoorthi V.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Media Content ◽  
Media Content Analysis ◽  
Comprehensive Overview ◽  
Case Examples ◽  
The Social ◽  
The Right ◽  
Audio Video ◽  
Suitable Case

Today's wealth and value are data. Data, used sensibly, are making wonders to make wise decisions for individuals, corporates, etc. The era of spending time with an individual to understand them better is gone. Individual's interests, requirements are identified easily by observing the activities an individual performs in social media. Social media, started as a tool for interaction, has grown as a platform to make and promote business. Social media content is unavoidable as the data that are going to be dealt with is huge in volume, variety, and velocity. The demand for using machine learning in analysing social media content is increasing at a faster pace in identifying influencers, demands of individuals. However, the real complexity lies in making the data from social media suitable for analysis. The type of data from social media content may be audio, video, image. The chapter attempts to give a comprehensive overview of the various pre-processing methods involved in dealing the social media content and the usage of right algorithms at the right time with suitable case examples.

scholarly journals Quantifying Suicidal Ideation on Social Media using Machine Learning: A Critical Review

2021 ◽  
pp. 4092-4100
Author(s):  
Syed Tanzeel Rabani ◽  
Qamar Rayees Khan ◽  
Akib Mohi Ud Din Khanday
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Suicidal Ideation ◽  
Social Isolation ◽  
Suicide Prevention ◽  
Online Social Networks ◽  
Public Information ◽  
Machine Learning Techniques ◽  
Suicidal Thoughts

Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took the harsh step of suicide. The last Instagram posts revealed signs of depression, which if anticipated could have saved the precious life. Recent research indicated that the public information on social media provides valuable insights on detecting the users with the suicidal ideation. The motive of this study is to provide a systematic review of the work done already in the use of social media for suicide prevention and propose a novel classification approach that classifies the suicide related tweets/ posts into three levels of distress. Moreover, our proposed classification task which was implemented through various machine learning techniques revealed high accuracy in classifying the suicidal posts. Among all algorithms, the best performing algorithm was that of the decision tree, with an F1 score ranging 0.95-0.97. After thoroughly studying the work achieved by different researchers in the area of suicide prevention, our study critically analyses those works and finds various research gaps and solves some of them. We believe that our work will motivate research community to look into other gaps that will in turn help psychiatrists, psychologists, and counsellors to protect individuals suffering from suicidal ideation.

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.

scholarly journals Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

10.2196/30753 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e30753
Author(s):  
Mai ElSherief ◽  
Steven A Sumner ◽  
Christopher M Jones ◽  
Royal K Law ◽  
Akadia Kacha-Ochana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Addiction Treatment ◽  
Opioid Use Disorder ◽  
Supervised Machine Learning ◽  
Computational Techniques ◽  
Opioid Use ◽  
Web Based ◽  
Health Communities

Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

scholarly journals Cyber Bullying Detection on Social Media using Machine Learning

2021 ◽  
Vol 40 ◽  
pp. 03038
Author(s):  
Aditya Desai ◽  
Shashank Kalaskar ◽  
Omkar Kumbhar ◽  
Rashmi Dhumal
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Self Esteem ◽  
Young Generation ◽  
Health Issues ◽  
The Past ◽  
The Individual ◽  
Deep Learning Model

Usage of internet and social media backgrounds tends in the use of sending, receiving and posting of negative, harmful, false or mean content about another individual which thus means Cyberbullying. Bullying over social media also works the same as threatening, calumny, and chastising the individual. Cyberbullying has led to a severe increase in mental health problems, especially among the young generation. It has resulted in lower self-esteem, increased suicidal ideation. Unless some measure against cyberbullying is taken, self-esteem and mental health issues will affect an entire generation of young adults. Many of the traditional machine learning models have been implemented in the past for the automatic detection of cyberbullying on social media. But these models have not considered all the necessary features that can be used to identify or classify a statement or post as bullying. In this paper, we proposed a model based on various features that should be considered while detecting cyberbullying and implement a few features with the help of a bidirectional deep learning model called BERT.

scholarly journals Machine learning and natural language processing in mental health: a systematic review (Preprint)

2019 ◽  
Author(s):  
Aziliz Le Glaz ◽  
Yannis Haralambous ◽  
Deok-Hee Kim-Dufor ◽  
Philippe Lenca ◽  
Romain Billot ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Clinical Practice ◽  
Natural Language ◽  
Language Processing ◽  
Care Providers ◽  
Medical Databases

BACKGROUND Machine learning (ML) systems are parts of Artificial Intelligence (AI) that automatically learn models from data in order to make better decisions. Natural Language Processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE The primary aim of this systematic review is to summarize and characterize studies that used ML and NLP techniques for mental health, in methodological and technical terms. The secondary aim is to consider the interest of these methods in the mental health clinical practice. METHODS This systematic review follows the PRISMA guidelines and is registered on PROSPERO. The research was conducted on 4 medical databases (Pubmed, Scopus, ScienceDirect and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, mental disorder. The exclusion criteria are: languages other than English, anonymization process, case studies, conference papers and reviews. No limitations on publication dates were imposed. RESULTS 327 articles were identified, 269 were excluded, and 58 were included in the review. Results were organized through a qualitative perspective. Even though studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into three categories: patients included in medical databases, patients who came to the emergency room, and social-media users. The main objectives were symptom extraction, severity of illness classification, comparison of therapy effectiveness, psychopathological clues, and nosography challenging. Data from electronic medical records and that from social media were the two major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than "transparent” functioning classifiers. Python was the most frequently used platform. CONCLUSIONS ML and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new knowledge,. and one major category of the population, social-media users, is obviously an imprecise cohort. In addition, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, ML and NLP techniques provide useful information from unexplored data (i.e., patient’s daily habits that are usually inaccessible to care providers). This may be considered to be an additional tool at every step of mental health care: diagnosis, prognosis, treatment efficacy and monitoring. Therefore, ethical issues – like predicting psychiatric troubles or involvement in the physician-patient relationship – remain and should be discussed in a timely manner. ML and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice. CLINICALTRIAL Number CRD42019107376

Sign in / Sign up

Export Citation Format

Share Document