## 3 Materials and Methods
### Dataset
#### 3.1.6 Dataset Organisation  
Our dataset is an organized collection of Reddit posts, stemming from five key subreddits related to mental health: r/anxiety, r/depression, r/mentalhealth, r/SuicideWatch, and r/loney. These subreddits were selected for their focused discussions on mental health, providing a valuable pool of data for relevant research. Figure 1: Number of sample posts per subreddit. Source: authors’ compilation, based on data collected from Reddit. Data reflects samples gathered in January 2020, January 2021, and January 2022. Figure 2: Total number of extracted posts per subreddit. Source: authors’ compilation, based on data extracted from selected subreddits on Reddit from January 2019 to August 2022. \begin{table}
\begin{tabular}{c c} \hline \hline
**Title** & **Post Text** \\ \hline \multirow{4}{*}{lonely as hell} & _“I feel like as I am a pathetic loser. I don’t feel like if any of my friend care about me. I’m always the one sitting alone, and no one invite me to sit with them or take part in anything, and I know they all prefer each other over me. I don’t know what wrong am ng. I want to live my life without worries. There must be something wrong with me I feel like a fresh. It’s making my depression worse. I am always on the verge of tears. I don’t know what to do with myself I have no one.”_ \\ \cline{1-1} \cline{2-3}  & Source: Example post selected by authors to represent average length from data collected on Reddit, encompassing the time period from January 2019 to August 2022._ \\ \cline{1-1} \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 3: Average post length example. We have organized the dataset chronologically, dividing it into annual folders spanning from January 2019 to August 2022. Each year is further segmented into months, with corresponding monthly folders containing subreddit-specific CSV files. Our complete dataset is divided into two major sections. The first section (Part A) encompasses the raw Reddit posts in their totality. However, the second section (Part B) serves as the cornerstone of our dataset, containing a carefully annotated subset of posts. Structure of Part A--Raw Data  
Each CSV file in our dataset includes the following columns, providing a detailed view of the Reddit posts along with essential metadata:  
* **Author:** The username of the Reddit post's author. * **Created UTC** (Coordinated Universal Time): The UTC timestamp of when the post was created. * **Score:** The net score (upvotes minus downvotes) of the post. * **Selftext:** The main text content of the post. * **Subreddit:** The subreddit from which the post was sourced. * **Title:** The title of the Reddit post. * **Timestamp:** The local date and time when the post was created, converted from the UTC timestamp. This structured approach allows researchers to conduct detailed, time-based analyses and to easily access data from specific subreddits. Structure of Part B--Labelled Data  
Part B of our dataset, which includes a subset of 800 manually annotated posts, is structured differently to provide focused insights into the mental health discussions. The columns in Part B are as follows:  
* **Score**
* **Selftext**
* **Subreddit**
* **Title**
* **Label:** The assigned label indicating the identified root cause of mental health issues, based on our annotation process. The core strength of our dataset lies in this labeled section (Part B), where we have manually labeled 800 posts, according to the following four critical root cause categories:  
* Drug and alcohol;
* Early life;
* Personality factors;
* Trauma and stress. These categories represent key factors that contribute to mental health issues. This labelled subset of posts is of significant value, allowing researchers to delve deeper into the root causes of mental health concerns and to gain richer insights. In compliance with privacy and ethical standards, any mentions of ages 1-18 in posts have been redacted. The posts themselves have been retained in the dataset, but any reference to this age range has been carefully removed.