## 1 Introduction  
Mental health disorders represent one of the most prevalent illnesses globally, and are closely linked to an increased risk of suicide [1, 2]. According to the World Health Organization (WHO), mental health issues cost approximately USD 2.5 trillion in 2010, with an estimated increase of USD 6.0 trillion predicted by 2030, as more than 350 million people are impacted by depression [3]. In Australia alone, half of the population faces mental health challenges, with about 3000 individuals tragically ending their lives each year [4]. From 2011 to 2021, suicide rates in males and females increased from 16.2 to 18.6 and from 5.1 to 5.8 deaths per one hundred thousand, respectively, [5]. Considering these statistics, it is evident that mental health problems have impacted the world and that only a fraction of people have received timely and ample treatment. This clearly indicates future economic strain on governments and requires new modes of intervention and prevention strategies to reduce mental illness and suicide. The early detection of root causes is a crucial step in employing preventive measures [6]. Traditional clinical settings have primarily relied on designed questionnaires and conventional techniques [7]. However, this approach often captures data only after the full development of the illness, at which point many individuals may be reluctant to seek treatment [8]. The limitations of these traditional methods, along with the rising frequency of undetected cases despite decades of research, emphasise the need for more proactive strategies in mental health diagnostics and treatment [9]. The advent of social media has opened new avenues for analyzing mental health disorders. In our interconnected world, where personal narratives are shared openly, social media platforms have become a valuable resource for understanding the daily stresses and concerns of people globally. Particularly during the COVID-19 pandemic, social media has provided unprecedented insights into mental health trends. Globally, approximately one in eight individuals grapple with some form of mental disorder, including but not limited to anxiety, depression, Post-Traumatic Stress Disorder (PTSD), and bipolar disorder [10]. These disorders often have deep-rooted causes, extending beyond immediate or obvious factors. Recognizing the complexity of these disorders, Health Direct Australia identifies several key root causes [11]. These include:  
* Personality factors;
* Drug and alcohol abuse;
* Trauma and stress factors;
* Early life environment;
* Biological factors;
* Genetic factors. Our core hypothesis posits that social media platforms offer a rich source for understanding the root causes of mental health issues. The format of social media posts enables in-depth textual sharing of mental health narratives, thereby creating a dataset not attainable through traditional surveys. This dataset provides the opportunity to train natural language processing (NLP) models for the effective and accurate identification of mental health root causes from regular conversations. A secondary aspect of our hypothesis involves the potential shifts in mental health root causes across different time periods. By extracting data from pre-pandemic, pandemic, and post-pandemic timelines, we aim to investigate any significant changes in factors affecting mental health. The exploration of data across various time periods will enrich the analysis, offering a broader perspective on how these root causes may have evolved due to significant global events like the COVID-19 pandemic. To explore this hypothesis, our research poses the following key questions:  
**RQ1:**: How can a semantically comprehensive corpus be created that captures factors or sentiments related to mental health during pre-pandemic, pandemic, and post-pandemic time periods? **RQ2:**: What methodologies can be developed to accurately annotate and categorize mental health-related discussions on social media?