## 7 Conclusions and Future Work  
In this paper, we made a substantial contribution to the field of mental health discourse analysis by creating a large-scale dataset. This dataset not only provides an in-depth view of mental health discussions on social media but also lays the groundwork for the identification of root causes of mental health issues.  
By shifting the focus from merely detecting the presence of mental health disorders to identifying their root causes, our research offers a more comprehensive understanding of mental health discourse online. This approach has the potential to significantly enhance mental health interventions by providing insights into the subtle linguistic patterns indicative of mental distress and the contextual factors influencing mental health discourse.  
Looking to the future, we see immense potential for future research in this area. The emergence of transformer-based models has opened up new possibilities for extracting more insights from our annotated dataset. These advanced models, renowned for their ability to understand context and semantics in text data, could potentially enhance our understanding of mental health discussions online.  
Moreover, the potential of our carefully annotated dataset extends to the development of predictive models. These models can be instrumental in identifying early indicators of mental health issues from user-generated content. For instance, by employing machine learning algorithms, we could predict the likelihood of a user experiencing a mental health crisis based on their social media posts. This predictive capability could be pivotal in facilitating timely interventions, offering support to individuals at critical moments.  
In conclusion, our large dataset, complemented by a rigorously annotated subset, is a valuable resource for future research, opening up exciting possibilities for leveraging advanced machine learning models and predictive analytics to enhance our understanding of and intervention in mental health discussions online. We believe that our work represents a significant step forward in the field of mental health research, and we look forward to seeing how it will be utilized and built upon in the future.  
Conceptualization, S.R. and K.A.; methodology, S.R., K.A. and S.S.; validation, S.R. and K.A.; writing, S.R.; writing review and editing, K.A.; supervision, K.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the National Health and Medical Research Council (NHMRC) National Statement on Ethical Conduct in Human Research (2007) Updated 2018, and was approved by the Victoria University Human Research Ethics Committee on 29 May 2023 (ID: HRE23-005).  
Not applicable.  
Data Availability Statement: The dataset is publicly available and can be accessed on Kaggle at [[https://rb.gy/ewtify](https://rb.gy/ewtify)]([https://rb.gy/ewtify](https://rb.gy/ewtify)), accessed on 17 December 2023.  
The authors extend their profound gratitude to [PERSON] for her invaluable contributions to this study. [PERSON] extensive experience as a consultant psychiatrist, particularly her work addressing mental health implications in diverse cultural contexts, has been pivotal in guiding the research methodology. Her expertise in the intersections of mental health with gender and race, coupled with her roles at the University of Melbourne, UNSW School of Social Sciences, and as the Chair of the Family Violence Psychiatry Network at the Royal Australian New Zealand College of Psychiatrists, significantly enhanced the data annotation process. Her commitment to address unique mental health challenges across different populations has greatly influenced the depth and breadth of this research.  
The authors declare that they have no financial interests related to this research, as it did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.