Data Augmentation for Mental Health Classification on Social Media (2021)

Abstract: The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user-generated text on social media platforms. Academic researchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain-specific user-generated text for mental health classification. Among the existing well-established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back-Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers- Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experimental results show significant improvements in classifiers’ performance when trained on the augmented data.

## introduction recent studies over mental health classification @xcite @xcite convey that amid covid-19 pandemic, the number of stress, anxiety and depression related mental disorders have increased. as per the recent survey, the rate of increase of mental disorders is more than those of physical health impacts on the chinese population @xcite . in this context, the early detection of psychological disorders is very important for good governance. it is observed that more than 80% of the people who commit suicide, disclose their intention to do so on social media @xcite . clinical depression is the result of frequent tensions and stress. further, prevailing clinical depression for a longer time period results in suicidal tendencies. the information mining from social media helps in identifying stressful and casual conversations @xcite @xcite . many machine learning (ml) algorithms are developed in literature using both automatic and handcrafted features for classifying microblog. the problem of data sparsity is underexplored for mental health studies on social media due to the sensitivity of data @xcite . multiple ethical clearances are required for new developments in mental health classification. to deal with this issue of data sparsity, we have used data augmentation techniques to multiply the training data @xcite . the increase in training data may help to improve the hyper-parameter learning of textual features and thereby, reducing overfitting. data augmentation is the method of increasing the data diversity without collecting more data @xcite . the idea behind the use of data augmentation (da) techniques is to understand the improvements in training classifiers for mental health detection on social media. in this manuscript, the mental health classification is performed for two datasets to test the scalability of data augmentation approaches for mental healthcare domain. the classification of casual and stressful conversations @xcite , and classifying depression and suicidal posts @xcite on social media. we select a rule based approach which preserves the original label and diversifies the text. to the best of our knowledge, this is the first attempt of stuffing additional data for mental health classification and there is no such study in the existing literature. the key contributions of this work are as follows: ethical clearance: we use limited, sparse and publicly available dataset for this study and so, no ethical approval is required from the institutional review board (irb) or elsewhere. we organize rest of the manuscript in different sections. section 2 describes the historical perspective of data augmentation and mental health classification on social media. we discuss the data augmentation methods and the architecture for experimental setups in section 3. section 4 elucidates the experimental results and evaluation over the proposed architecture of experimental setup which shows the significance and feasibility of data augmentation over mental health classification problems. finally, section 5 gives the conclusion and future scope of this work. ## related work mental health classification can be quite challenging without the availability of sufficient data. although the users' posts can be extracted from the social media platforms such as reddit, twitter and facebook, annotating these posts is quite expensive. to address this issue, researchers have proposed different data augmentation techniques suitable for natural language processing (nlp) which varies from simple rule-based methods to more complex generative approaches @xcite . the data augmentation tasks is categorized into conditional and unconditional augmentation task @xcite . ## background: data augmentation methods data augmentation @xcite is a recent technique used for nlp to handle the problem of data sparsity by increasing the size of the training data without explicitly collecting the data. in this section, we describe three potential textual data augmentation techniques, problem formulation, and architecture of the experimental setup. ## experimental results and evaluation in this section, we discuss the datasets and the experimental results. we further analyze results for data diversity and statistical significance of the classifiers over augmented data as compared to the original data. ## conclusion in this work, we use the data augmentation approach for mental health classification on two different social media datasets. the experimental results using logistic regression classifier and doc2vec embedding shows significant improvements in f1 score and precision with augbert. to tackle the problem of data sparsity and support the automation of the 3-step theory over social media data @xcite , the data augmentation over mental healthcare may give remarkable results. in future, we are planning to use other domain-specific libraries and neural machine translation for explainable and conditional data augmentation.