An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts (2023)

Abstract: With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. The success of computational intelligence techniques for inferring mental illness from social media resources, points to natural language processing as a lens for determining Interpersonal Risk Factors (IRF) in human writings. Motivated with limited availability of datasets for social NLP research community, we construct and release a new annotated dataset with human-labelled explanations and classification of IRF affecting mental disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii) Perceived Burdensomeness (PBu). We establish baseline models on our dataset facilitating future research directions to develop real-time personalized AI models by detecting patterns of TBe and PBu in emotional spectrum of user’s historical social media profile.

## introduction the world health organization (who) emphasizes the importance of significantly accelerating suicide prevention efforts to fulfill the united nations' sustainable development goal (sdg) objective by 2030 @xcite . reports released in august 2021 1 indicate that 1.6 million people in england were on waiting lists for mental health care. an estimated 8 million people were unable to obtain assistance from a specialist, as they were not considered sick enough to qualify. as suicide remains one of the leading causes of the death worldwide 2 , this situation underscores the need of mental health interpretations from social media data where people express 1 https://www.theguardian.com/society/2021/ aug/29/strain-on-mental-health-care-leaves-8mpeople-without-help-say-nhs-leaders 2 https://news.un.org/en/story/2021/06/1094212 ## experimental settings for consistency, we used the same experimental settings for all models and split the dataset into the train, validation, and test sets. all results are reported on the test set, which makes up 30% of the whole dataset. we used the grid search optimization technique to optimize the parameters. to tune the number of layers (n), we empirically experimented with the values: learning rate (lr): @xmath0.001, 0.0001, 0.00001} and optimization (o): @xmath1, 'adamax', 'adamw'} with a batchsize of 16, 32 were used. we used base version pre-trained language models (lms) using hugging-face foot_9 , an open-source python library. we used optimized parameters for each baseline to find precision, recall, f1-score, and accuracy. varying lengths of posts are padded to 256 tokens with truncation. each model was trained for 20 epochs, and the best-performing model based on the average accuracy score was saved. thus, we set hyperparameter for our experiments as @xmath2, learning @xmath3, batch @xmath4, and @xmath5. ## conclusion and future work we present a new annotated dataset for discovering interpersonal risk factors through human-annotated extractive explanations in the form of text-spans and binary labels in 3522 english reddit posts. in future work, we plan to enhance the dataset with more samples and develop new models tailored explicitly to tbe and pbu. the implications of this work include the potential to improve public health surveillance and other mental healthcare applications that rely on automatically identifying posts in which users describe their mental health issues. we keep the implementation of explainable ai models for multi-task text classification, as an open research direction for open ai and other newly developed responsible ai models. we pose the discovery of new research directions for future, through longitudinal study on users' historical social media profile to examine interpersonal risk factors and potential risk of self-harm or suicidal ideation. as we focus on reddit data as a starting point of our study, exploring other forums could be an interesting research direction. bilitation counselor, for their unwavering support throughout the project. additionally, we extend our heartfelt appreciation to prof. sunghwan sohn for his consistent guidance and support. this project was partially supported by nih r01 ag068007. this project is funded by nserc discovery grant (rgpin-2017-05377), held by vijay mago, department of computer science, lakehead university, canada.