## 6 Conclusions  
This study proposes a method to detect signs of depression within digital content. The proposed approach is based on traditional machine learning algorithms to yield substantial performance without compromising interpretability and computational efficiency. To this aim, we introduce a comprehensive feature framework that organizes features into four distinct sets: affective, topic, social, and syntactic. Such a framework is combined with distributional representations that capture semantic information from the text. Specifically, context-dependent word embeddings (utilizing all-distilroberta-v1 and all-mpnet-base-v2 models) and context-independent word embeddings (FastText) are used to assess their impact on performance. The effectiveness of the proposed approach is evaluated with an experimental study using two public English datasets from social media platforms. The experiments demonstrate that the proposed approach enables traditional classifiers to yield substantial depression detection performance, with an F1-scoreof 74.38% in the DsD dataset and 63.94% in the DepSign dataset. These results are comparable to those obtained using more complex methods such as deep neural networks and transformer-based models. In addition, the relevance of these outcomes is confirmed by a statistical analysis. The advances presented here hold potential for the early detection of depression. One of the key strengths of our approach lies in its ability to strike a balance between performance and computational resources, owing to its reliance on simple yet effective features. Additionally, the interpretability of our approach enables a better understanding of the influence of social, personal, and affective factors on depression, leading to the identification of appropriate regulatory measures. Based on the encouraging results obtained, our research opens several research directions. First, our existing solution demonstrates the potential for seamless deployment in real-world environments, offering substantial performance without high computational resources. This aspect could be exploited by developing a user-friendly tool that integrates our approach to aid in the early detection of depression in text. Furthermore, as part of our future work, we intend to explore the expansion of the feature framework to incorporate other types of features that have shown promising results in various classification tasks, such as moral values [75]. Finally, we see potential in extending the applicability of our method to other languages, broadening its scope and impact. Conceptualization, S.M. and C.A.I. ; methodology, S.M. and C.A.I. ; software, S.M. ; writing--original draft preparation, S.M. ; writing--review and editing, S.M. and C.A.I. ; visualization, S.M. ; project administration, C.A.I. ; funding acquisition, C.A.I. All authors have read and agreed to the published version of the manuscript. This work is partially supported by AROMA, a subproject of the MIRATAR project (TED2021-132149B-C4), funded by Spanish Ministry of Science and Innovation and by the European Union with NextGeneration EU funds (AEI/10.13039/501100011033/); and by project AMOR, funded by Spanish Ministry of Economic Affairs and Digital Transformation through the program UNICO I+D Cloud in the framework of the Recovery, Transformation and Resilience Plan (RTRP) and by the European Union NextGeneration EU funds within the Recovery and Resilience Facility (RRF). Not applicable. The code and data are available at [[https://github.com/gsi-upm/depression-sl-text](https://github.com/gsi-upm/depression-sl-text)]([https://github.com/gsi-upm/depression-sl-text](https://github.com/gsi-upm/depression-sl-text)), accessed on 16 August 2023. We would like to thank the authors of the datasets used for their work and for providing free access to them, [PERSON] et al. [30], [PERSON] et al. [32] and [PERSON] et al. [31]. The authors declare no conflict of interest.