Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores
Authors: Ana-Maria Bucur, Liviu P. Dinu (University of Bucharest, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
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 achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.

Introduction
Mental illnesses are a common problem of our modern world. More than one in ten people was living with mental health disorders in 2017 AUTHOR, with women being the most affected. These disorders affect people's way of thinking, mood, emotions, behaviour and their relationships with others. Most mental illnesses remain undiagnosed because of the social stigma around them. Depression is one of the main causes of disability globally, it affects people of all ages. Prevention is used to reduce depression and to save the lives of people at risk of suicide, but prevention is only limited to raising awareness and programs to cultivate positive thinking in case of depression and monitoring people who attempted suicide or self-harm. With the rise in social media use, more computational efforts are made to detect mental illnesses such as depression AUTHOR and PTSD AUTHOR, but also to detect misogyny AUTHOR, irony and sarcasm AUTHOR from users' texts. People tend to talk more about their emotions and mental health problems online and to seek support. The sources of mental health cues used for detection are Twitter, Facebook, Reddit and forums AUTHOR. Reddit is a social media site very similar to forums. It is organized in subreddits with specific topics, some dedicated to mental health problems. The use of throwaway accounts to maintain anonymity promotes disclosure, and users are more likely to share problems they have not discussed with anyone before. The use of these accounts makes it difficult for users to receive more social support because the majority of them are used only for one post AUTHOR. In this work, we choose to tackle the problem of detecting early onset of depression from users' posts on social media, specifically from Reddit. As such, we explore the eRisk 2018 dataset through topic analysis by means of Latent Semantic Indexing AUTHOR and learned out-of-distribution confidence scores AUTHOR. Due to the nature of the dataset, we repurpose the learned confidence score to make a decision on whether to label the user as depressed or non-depressed or to wait for more data, as test chunks were progressively released every week.

Conclusion
In this paper, we use the eRisk 2018 dataset on Early Detection of Signs of Depression for depression classification from Reddit posts. Our method uses Latent Semantic Indexing for topic modelling and to generate the embeddings used as input for our neural network, but focuses on using a learned out-of-distribution confidence score alongside the classification output to decide whether to label the user or wait for more data. Besides its initial use case in out-of-distribution detection, we repurposed the confidence score as a measure for how much the model trusts its classification output to be correct. We showed that, in general, there is a significant difference in writing topics depending on the users’ mental health, to the extent that it contains enough information for use in classification.