A Deep Learning Framework for Detecting Depression Tendencies in Social Media Content

Year 2025, Volume: 6 Issue: 1, 42 - 47, 29.06.2025

Sevda Güneyli , Serpil Aslan

https://doi.org/10.46572/naturengs.1700871

Abstract

This study aims to detect depression-related content in social media posts automatically. The Sentiment140 dataset was used as the data source, with the text data preprocessed using natural language processing techniques. The processed texts were then converted into numerical vector representations using the GloVe word embedding method and analyzed with various classification algorithms. Deep learning models (CNN, LSTM, BiLSTM) and traditional machine learning algorithms (Naive Bayes, Random Forest, Decision Tree, SVM, Logistic Regression) were evaluated and compared for performance. Evaluation metrics included accuracy, precision, recall, and the F1 score. The experimental results show that deep learning models, particularly the CNN architecture, outperformed traditional classification methods in detecting depression-prone content. Overall, the study presents a practical and applicable approach for conducting mental health analysis based on social media data.

Keywords

Depression Detection , Social Media Analysis , GloVe , Deep Learning , Machine Learning , Text Classification , Sentiment140 , Sentiment Analysis

Supporting Institution

This study was supported by the Scientific Research Projects Coordination Unit of Malatya Turgut Özal University under project number 25Y05.

Project Number

25Y05

Thanks

This study was supported by the Scientific Research Projects Coordination Unit of Malatya Turgut Özal University under project number 25Y05.

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