## 3 Materials and Methods
### A Feature Framework for Depression Detection from Text  
In this section, we describe a feature framework designed to facilitate the systematic organization and characterization of textual characteristics for the purpose of detecting depression. This framework is based on a previously designed stress framework [26] but was extended and adapted for its use in the depression detection context. To build a comprehensive framework that facilitates the identification of appropriate feature sets, we have used established lexicons and text analysis methodologies to extract a vast amount of features from the text. Most of these features were extracted from a set of distinct lexicons using SEANCE [54]. This tool enables the automatic analysis of affective and social values in text using the following lexicons: General Inquirer (GI) [55], Lasswell [56], Geneva Affect Label Coder (GALC) [57], Affective Norms for English Words (ANEW) [58], EmoLex [59], SenticNet [60], Valence Aware Dictionary for Sentiment Reasoning (VADER) [61], and Hu-Liu [62]. Furthermore, the proposed framework incorporates Linguistic Inquiry and Word Count (LIWC) [63], a widely recognized lexicon-based tool that provides comprehensive scores for psychologically relevant categories, including, but not limited to, joy, sadness, and specific cognitive processes. Finally, a broad set of readability measures were incorporated using the Python Readability library [64]. This library offers traditional readability measures (that is, Automated Readability Index (ARI) [65] and Simple Measure of Gobbledygook (SMOG) [66] indexes) based on the number of words, syllables, and sentences. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **DsD** & **DepSign** \\ \hline No. posts & 3553 & 10,251 \\ \hline Avg. word count & 88 & 171 \\ \hline Avg. char count & 442 & 840 \\ \hline Labels & minimal (2587), mild (290), moderate (394), severe (282) & not depressed (3503), moderate (5780), severe (968) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the used datasets. As a result, a comprehensive set of nearly 400 distinct features is obtained that encompass various types and characteristics. To effectively examine the impact of these characteristics based on their respective attributes, we have partitioned them into four distinct sets: affective, social, syntactic, and topic-related. The affective set encompasses those characteristics related to sentiments (positive, negative, or neutral), emotions (e.g., surprise, happiness, sadness), and moods (e.g., distress, attention, unease). All considered lexicons contribute to this set, providing a total of 113 features, including fear, anxiety, and arousal, among others. The social set contains characteristics related to interpersonal relationships, such as trust, deception, and gratitude. Extracted from LIWC, GALC, GI, Laswell, and EmoLex, the social set comprises 52 characteristics. The syntactic set incorporates 90 features related to stylistic aspects, including tone, self-reference, and writing fluency, to shed light on how the text was constructed and structured. These features are extracted mainly from the Readability Python library but also include some features provided by the LIWC and GI lexicons. Finally, the set of topics consists of 143 features that identify different themes within the text. Features such as horror, aggression, and work are derived from the LIWC, Laswell, and GI dictionaries, providing a comprehensive view of the various topics prevalent in the text. An overview of these feature sets, along with some examples of each, is given in Figure 1. The proposed categorization allows us to discern and analyze the unique contributions of each type of characteristic, providing valuable information on their roles in the context of text-based depression detection. For this purpose, we conducted a study computing the information gain of the different feature sets.