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Journals / Sinop Üniversitesi Fen Bilimleri Dergisi / 2024 / Cilt: 9 Sayı: 2

Using Deep Learning Techniques to Identify Depression in Social Media Interactions

Sosyal Medya Etkileşimlerinde Depresyonu Tanımlamak için Derin Öğrenme Tekniklerinin Kullanılması

Ördek İbrahim Nooruldeen0009-0004-9477-1187
Serkan Savaş0000-0003-3440-6271
Pages
449–466
Publication Date
2024-12-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This research provides a foundation for advanced predictive analytics in mental health monitoring and highlights the potential of Bi-LSTM in improving the accuracy and efficiency of depression detection models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Depression is one of the most common mental problems and an important cause of suicide.
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Key Findings & Data:The increased use of social media platforms has enabled early diagnosis of depression through the sentences expressed by users using everyday language.
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Conclusion & Impact:This research provides a foundation for advanced predictive analytics in mental health monitoring and highlights the potential of Bi-LSTM in improving the accuracy and efficiency of depression detection models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sosyal Medya · Depresyon Sınıflandırması · Twitter

Abstract

Depression is one of the most common mental problems and an important cause of suicide. The increased use of social media platforms has enabled early diagnosis of depression through the sentences expressed by users using everyday language. As social media platforms continue to play a central role in individuals' daily lives, there is a growing interest in using these platforms for mental health analysis. In this study, Twitter (nowadays X) was used to categorize depression. The aim of this study is to identify tweets with depressive characteristics among tweets retrieved from Twitter. We present an innovative model for depression prediction using a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. This model utilizes appropriate cleaning and preprocessing techniques for more accurate detection of depression using language features in tweets. For the study, a special dataset obtained through Twitter API was created and analyses were performed on this dataset. The proposed Bi-LSTM model has shown remarkable effectiveness, achieving an accuracy rate of 97.22%. The results obtained demonstrated the feasibility and effectiveness of deep learning techniques for recognizing depression-related patterns in the emotions of Twitter users. This research provides a foundation for advanced predictive analytics in mental health monitoring and highlights the potential of Bi-LSTM in improving the accuracy and efficiency of depression detection models.

Özet

Depresyon, en yaygın zihinsel sorunlardan biridir ve intiharların önemli bir nedenidir. Sosyal medya platformlarının kullanımının artması, kullanıcıların günlük dilini kullanarak ifade ettikleri cümleler üzerinden depresyonun erken teşhisine olanak sağlamıştır. Sosyal medya platformlarının bireylerin günlük hayatlarında merkezi bir rol oynamaya devam etmesiyle, bu platformları ruh sağlığı analizi için kullanma konusunda artan bir ilgi bulunmaktadır. Bu çalışmada, Twitter (günümüzde X) üzerinden depresyon sınıflandırması yapılmıştır. Bu çalışmanın amacı Twitter'dan alınan tweetler arasında depresif özellik taşıyan tweetleri tespit etmektir. Çalışmada, Çift Yönlü Uzun Kısa Süreli Bellek (Bi-LSTM) mimarisi kullanarak depresyon tahmini için yenilikçi bir model sunulmuştur. Bu model, tweetlerdeki dil özelliklerini kullanarak depresyonun daha doğru tespiti için uygun temizleme ve ön işleme tekniklerinden faydalanmaktadır. Çalışma için, Twitter API yoluyla elde edilen özel bir veri seti oluşturulmuş ve analizler bu veri seti üzerinde gerçekleştirilmiştir. Önerilen Bi-LSTM modeli, %97.22'lik bir doğruluk oranı elde ederek dikkate değer bir etkinlik göstermiştir. Elde edilen sonuçlar, Twitter kullanıcılarının duygularındaki depresyonla ilgili örüntüleri ayırt etmek için derin öğrenme tekniklerinin kullanılabilirliği ve etkinliğini göstermiştir. Bu araştırma, ruh sağlığı izlemede ileri düzey tahmin analitikleri için bir temel oluşturmakta ve depresyon tespit modellerinin doğruluğunu ve verimliliğini artırmada Bi-LSTM'in potansiyelini vurgulamaktadır.

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Cite This Article

Nooruldeen, Ö. İ., Savaş, S. (2024). Using Deep Learning Techniques to Identify Depression in Social Media Interactions. Sinop Üniversitesi Fen Bilimleri Dergisi, Cilt: 9 Sayı: 2, 449–466. https://doi.org/10.33484/sinopfbd.1456956

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