Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

@article{Bucur2020DetectingEO,
  title={Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores},
  author={Ana-Maria Bucur and Liviu P. Dinu},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.01695}
}
Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and… 

Tables from this paper

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
TLDR
An extensive part-of-speech analysis of the discourse of social media users with depression, providing insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression
In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological
Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT
TLDR
The contributions of the BLUE team are presented in the 2021 edition of the eRisk workshop, in which they tackle the problems of early detection of gambling addiction, self-harm and estimating depression severity from social media posts.
Studies of depression and anxiety using Reddit as a data source: Scoping review (Preprint)
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
An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder
TLDR
This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end and allows for automatic dataset creation by identifying discriminating posts in a user’s text-set.

References

SHOWING 1-10 OF 35 REFERENCES
A Neural Network Approach to Early Risk Detection of Depression and Anorexia on Social Media Text
TLDR
The approach to early risk detection of depression and anorexia on social media in CLEF eRisk 2018 is presented, which combines TF-IDF information and convolutional neural networks (CNNs) to identify the articles written by potential patients.
Word Embeddings and Linguistic Metadata at the CLEF 2018 Tasks for Early Detection of Depression and Anorexia
TLDR
FHDO Biomedical Computer Science Group (BCSG) has submitted results obtained from four machine learning models as well as from a final late fusion ensemble based on user-level linguistic metadata, Bags of Words, neural word embeddings, and Convolutional Neural Networks.
Using Topic Extraction on Social Media Content for the Early Detection of Depression
TLDR
This work implements a system based on the topic extraction algorithm, Latent Dirichlet Allocation and simple neural networks, which uses uni-gram, bi-gram and tri-gram frequency to extract 30 latent topics in an unsupervised manner.
Predicting Depression via Social Media
TLDR
It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.
Temporal Mood Variation: at the CLEF eRisk-2018 Tasks for Early Risk Detection on The Internet
TLDR
The participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Micro´electronique de Montpellier) in both CLEF eRisk-2018 on predicting mental disorder using Users posts on Reddit is presented.
Analysis and Experiments on Early Detection of Depression
TLDR
The participation of the Telematics Research group from the University of A Coruña at the eRisk 2018 task on early detection of signs of depression is presented and the DMWD model outperforms the DMCD on terms of ERDE5, ranking in the top-10 submissions for this task.
Natural language processing in mental health applications using non-clinical texts†
TLDR
The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.
PEIMEX at eRisk2018: Emphasizing Personal Information for Depression and Anorexia Detection
TLDR
The approach considers that the sentences where users refer to themselves contain terms that better expose their interests and habits and, therefore, are able to reveal characteristics of their personality, and social and psychological states.
UPF's Participation at the CLEF eRisk 2018: Early Risk Prediction on the Internet
TLDR
The participation of the Web Science and Social Computing Research Group from the Universitat Pompeu Fabra, Barcelona (UPF) at CLEF 2018 eRisk Lab is described, which presents several machine learning models that rely on features based on linguistic information, domain-specific vocabulary and psychological processes.
...
...