Augmenting R eddit Posts to Determine Wellness Dimensions impacting Mental Health (2023)

Abstract: Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative AI techniques for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative AI models, and evaluate the ROUGE scores and syntactic/ semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation (EDA) and Backtranslation (BT).

## introduction the social determinants of health (sdoh) refer to various factors present in the surroundings where individuals are born, reside, acquire knowledge, work, engage in leisure activities, practice religion, grow older, impacting a broad range of health-related outcomes, risks and quality-of-life indicators. 23 a rapid expansion of research in sdoh 2030 encourages the social nlp research community to design and develop text 1 text 2 what's in your mind? i will always be alone in this world i am not fine social aspect with whom are you most connected to? i feel disconnected from almost everyone and everything alone user classifying wellness dimension wellness dimensions 1. physical aspect 2. intellectual and vocational aspect 3. social aspect 4. spiritual and emotional aspect intrinsic auxiliary task computational intelligence models for enhancement of an individual's well-being @xcite . in this work, we choose to prescreen human-writings for biomedical therapy by investigating latent indicators of wellness dimensions in reddit posts (see illustration in figure 1 ). wellness dimensions (wd) refer to different aspects of an individual's overall wellbeing that contribute to their physical, spiritual, social, emotional, intellectual, occupational, environmental, and financial well-being. the disturbed wd, if remains unaddressed, have adverse impact on mental health of an individual. as social media becomes integral part of our daily lives @xcite , studies in the past suggest that individuals tend to express their thoughts and emotions impacted by one or more wellness dimensions more easily on social media platforms as compared to during in-person sessions with clinical psychol-ogists and mental healthcare @xcite . we construct, annotate and observe the original (natural) composition of wd dataset as an imbalanced dataset. in this work, we augment a multi-class dataset on wd to facilitate design and development of nlp models for classifying wd impacting mental health in reddit posts during mental health screening. pre-screening filters are helpful in biomedical therapy by facilitating early detection of wd impacting mental health, which if left untreated may cause severe mental disorders. dunn highlights holistic nature of wellness in 1961 as a high-level wellness, denoting a superior level of healthy living (printz-markó and ivancsóné horváth, 2017). we reduce multiple wd to four key dimensions of well-being based on the frequency and recognition in human writings: physical aspect (pa), intellectual and vocational aspect (iva), social aspect (sa), spiritual and emotional aspect (sea) @xcite . our major contributions (as illustrated in fig. 1 ) include (i) the applicability of generative nlp models for domain-specific data augmentation, (ii) examining the diversity among generated and original instances through semantic and syntactic similarity measure, (iii) test and validate the efficacy of data augmentation by investigating classifiers' performance. ## background according to @xcite , sociologists put forth a theory that outlines six social needs to prevent loneliness: attachment, social integration, nurturance, reassurance of worth, sense of reliable alliance, and guidance in stressful situations @xcite . the self-determination theory (sdt) foot_3 highlights the importance of balancing relatedness, competency, and autonomy for intrinsic motivation and genuine selfesteem, which contribute to overall well-being. neglecting mental disturbance can escalate subclinical depression to clinical depression by activating interpersonal risks. this research seeks to examine the origins and outcomes of mental disturbance to mitigate these risks. corpus construction we present a new dataset with 3,092 instances and 72,813 words to identify wellness dimensions impacting mental disturbance: pa, iva, sa, and sea. a senior clinical psychologist, a rehabilitation councilor, and a social nlp researcher framed annotation schemes and perplexity guidelines for text annotation through pre-defined wellness dimensions. our experts trained three postgraduate students to annotate the data based on predefined dimensions. the annotations were validated using fleiss' kappa interobserver agreement, resulting in a kappa score of 74.39%. final annotations were determined through majority voting and expert verification. the experts achieved a kappa score of 87.32% for the selection of explanatory text spans. despite slight confusion between pa and sea, there was a higher agreement for the selection of explanations. to facilitate future research and developments, we publicly release our dataset at github. foot_4 problem formulation: we collect and annotate reddit data from subreddits r/depression and r/suicidewatch for the task of identifying wd and found imbalanced dataset in its natural composition, suggesting the need of data augmentation. to evaluate the effectiveness of generative nlp models for data augmentation, we frame the task of augmenting reddit posts as a text generation problem. we compare and contrast the performance of model trained on data augmented with (i) generative pre-trained transformers (gpt) @xcite , and (ii) conventional data augmentation approach for nlp such as easy data augmentation (eda) @xcite and back translation (bt) @xcite . ## experiments and evaluation we first generate the data using two-fold measures: (i) traditional data augmentation methods for nlp -eda and bt, and (ii) prompt-based generative pre-trained transformer models @xcite . we further investigate the diversity of the generated samples in comparison to the original samples and fine-tune bert language model to observe improvements in wd classification, if any. hinged on the classification results and similarity measures, we select the best model for augmenting wd dataset. ## conclusion and future scope in this work, we augment the reddit posts for a four-class classification problem of determining wellness dimensions impacting mental health. the gpt models are outperforming in terms of generating diverse text by preserving the context of the corresponding original text. in future, we plan to experiment with different parameter settings and prompts for generating datasets and develop improved classifiers to determine wd in a well balanced dataset. furthermore, we will evaluate the classification performance of short explanation text we generated in this dataset.