2,756 research outputs found
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
An Automated Tool to Detect Suicidal Susceptibility from Social Media Posts
According to the World Health Organization (WHO), approximately 1.4 million
individuals died by suicide in 2022. This means that one person dies by suicide
every 20 seconds. Globally, suicide ranks as the 10th leading cause of death,
while it ranks second for young people aged 15-29. In the year 2022, it was
estimated that about 10.5 million suicide attempts occurred. The WHO suggests
that alongside each completed suicide, there are many individuals who make
attempts. Today, social media is a place where people share their feelings,
such as happiness, sadness, anger, and love. This helps us understand how they
are thinking or what they might do. This study takes advantage of this
opportunity and focuses on developing an automated tool to find if someone may
be thinking about harming themselves. It is developed based on the
Suicidal-Electra model. We collected datasets of social media posts, processed
them, and used them to train and fine-tune the model. Upon evaluating the
refined model with a testing dataset, we consistently observed outstanding
results. The model demonstrated an impressive accuracy rate of 93% and a
commendable F1 score of 0.93. Additionally, we developed an API enabling
seamless integration with third-party platforms, enhancing its potential for
implementation to address the growing concern of rising suicide rates.Comment: 8 pages, 10 figures, 1 table. Submitted to Peer
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Advances in large language models (LLMs) have empowered a variety of
applications. However, there is still a significant gap in research when it
comes to understanding and enhancing the capabilities of LLMs in the field of
mental health. In this work, we present the first comprehensive evaluation of
multiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, on
various mental health prediction tasks via online text data. We conduct a broad
range of experiments, covering zero-shot prompting, few-shot prompting, and
instruction fine-tuning. The results indicate a promising yet limited
performance of LLMs with zero-shot and few-shot prompt designs for the mental
health tasks. More importantly, our experiments show that instruction
finetuning can significantly boost the performance of LLMs for all tasks
simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5,
outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9%
on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%.
They further perform on par with the state-of-the-art task-specific language
model. We also conduct an exploratory case study on LLMs' capability on the
mental health reasoning tasks, illustrating the promising capability of certain
models such as GPT-4. We summarize our findings into a set of action guidelines
for potential methods to enhance LLMs' capability for mental health tasks.
Meanwhile, we also emphasize the important limitations before achieving
deployability in real-world mental health settings, such as known racial and
gender bias. We highlight the important ethical risks accompanying this line of
research
An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
With a surge in identifying suicidal risk and its severity in social media
posts, we argue that a more consequential and explainable research is required
for optimal impact on clinical psychology practice and personalized mental
healthcare. The success of computational intelligence techniques for inferring
mental illness from social media resources, points to natural language
processing as a lens for determining Interpersonal Risk Factors (IRF) in human
writings. Motivated with limited availability of datasets for social NLP
research community, we construct and release a new annotated dataset with
human-labelled explanations and classification of IRF affecting mental
disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii)
Perceived Burdensomeness (PBu). We establish baseline models on our dataset
facilitating future research directions to develop real-time personalized AI
models by detecting patterns of TBe and PBu in emotional spectrum of user's
historical social media profile
How can rural health be improved through community participation?
Executive summary
Rural Australians generally experience poorer health than their city counterparts. Rural Australia is a vast geographical region, with significant diversity, where there is good health and prosperity, as well as disadvantage. The purpose of this issue brief is to provide evidence on how the health of rural Australians can be improved through community participation initiatives, which are currently being funded and delivered by health services and networks.
Rural Australians need innovative health services that are tailored to the local context and meet increasing healthcare demands, without increases to expenditure. There are community participation approaches supported by research that can improve existing practice. Avoiding duplication, including the current work of Medicare Locals and Local Hospital Networks, is important for ensuring good outcomes from community participation initiatives.
The following recommendations are made to improve practice:
New ways to contract and pay for health services are needed, which use ideas developed with communities, within current budgets
State and federal government competitive grants and tenders should prioritise proposals that demonstrate effective community participation approaches
Community-based services, such as community health centres, Medicare Locals and Local Health Networks, have an important role to play in facilitating community participation, including:
Building partnerships between existing services and leveraging existing participation strategies, rather than developing new services or standalone initiatives—to leverage available funds and maximise outcomes
Employment of a jointly-appointed, paid community leadership position across existing community-based health services, to avoid duplication and overcome barriers of over-consultation and volunteer fatigue
Formal and robust evaluation of initiatives is necessary to guide future policy and research
A national innovative online knowledge sharing portal is required to share best practice in rural community participation, save time and money on ineffective approaches, and to support the rural health workforce
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