Cordyceps@ LT - EDI : Depression Detection with R eddit and Self-training (2023)

Abstract: Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression detection system using a semi-supervised learning technique. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.

## introduction 1.depression and social media a unique feature of depression is its effect on cognitive and verbal patterns. for example, depression diagnosis is correlated to the frequency of personal pronoun usage and the usage of positive-negative words @xcite . additionally, 1 https://www.reddit.com/ persons suffering from depression often connect vicious yet potentially fictional narratives to benign experiences, generally increasing the number of overwhelming situations they may experience @xcite . people may then go to social media and online forums like reddit to discuss and post about traumatizing experiences and may publicly reflect on their thoughts and behavior. it is unsurprising, therefore, to find a wealth of attempts (as surveyed by @xcite ) to use social media posts to create a potential diagnostic screening tool through language modeling. recently, language models can accurately predict symptoms before practitioners record them @xcite . there are significant challenges to data collection in the depression detection setting despite a potential abundance of data that likely exists on social media. indeed, excessive social media usage itself correlates with depression, adhd, and other serious mental health diagnoses @xcite . however, @xcite observe that most attempts at data collection rely on a self-declaration or a past diagnosis of depression, allowing for the possibility of non-actively depressed individuals creating depression-positive data. in this paper, we will attempt to apply an automatic data collection process from social media through a semi-supervised approach. ## related work recent work has demonstrated semisupervised learning techniques using unlabeled twitter data for depression detection as surveyed by @xcite . however, these studies tend to solely rely on twitter foot_0 data as their source of unlabeled texts @xcite . here, we will use reddit for our semi-supervised approach. @xcite also uses the reddit mental health dataset (low et al., ## methodology here we will provide the major implementation details of our solution in this section. see ## exploratory analysis we present an analysis of our generated pseudo-labels on the reddit mental health dataset @xcite . recall, that we assign a pseudo-label to a post only if the post is ranked in the top 30,000 in any of the three depression severity labels. in figure 1 we break down the distribution of the labels across the sources of these labels. notably, we find about 60% of our generated labels are contained in five subreddits: 'r/depression', 'r/adhd' 'r/suicidewatch', 'r/anxiety', 'r/mentalhealth'. in particular, the subreddit 'r/adhd' hosts the most pseudo-labels in the severe category out of any subreddit by a wide margin, account-ing for about 37% of all pseudo-labels in the category. there are multiple explanations for the above findings. indeed, adhd can co-occur with depression and can be seen as an early indication of a future depression diagnosis (meinzer and chronis-tuscano, 2017). additionally, adhd and depression have overlapping symptoms @xcite . thus, it is possible that there is some level of overlapping language or similar verbal processes shared between the two disorders. we encourage future work to explore alternative explanations and leverage this connection between adhd and depression in the depression-detection setting. ## conclusion in this paper, we present our framework based on self-training and demonstrate its performance on the detecting signs of depression from social media text -lt-edi@ranlp 2023 @xcite shared task. given the disparities observed in the development set and test set f1-score performance, future work should explore regulation techniques (e.g. augmentation and ensembling methods) to accompany the self-training approach. nonetheless, our approach still places 3rd overall in the shared task. with our use of pseudo-labeling on reddit, we highlighted adhd-focused forums as a major source of (non-diagnostic) severe depression classifications and discussed some explanations. we hope our work serves as a starting point for further investigation of the linguistic patterns of depression overlapping with other mental disorders.