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Depression detection from social network data using machine learning techniques

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Abstract

Purpose

Social networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools.

Methods

Although diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method.

Results

We report an implementation of the proposed method. We have evaluated the efficiency of our proposed method using a set of various psycholinguistic features. We show that our proposed method can significantly improve the accuracy and classification error rate. In addition, the result shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression.

Conclusions

Machine learning techniques identify high quality solutions of mental health problems among Facebook users.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh

    Md. Rafiqul Islam & Abu Raihan M. Kamal

  2. School of Computing and Mathematics, Charles Sturt University, Sydney, NSW, Australia

    Muhammad Ashad Kabir

  3. Department of Business Technology and Entrepreneurship, Swinburne University of Technology, Melbourne, Australia

    Ashir Ahmed

  4. Centre for Applied Informatics, Victoria University, Melbourne, Australia

    Hua Wang

  5. College of Engineering & Science, Victoria University, Melbourne, Australia

    Anwaar Ulhaq

Authors
  1. Md. Rafiqul Islam
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  2. Muhammad Ashad Kabir
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  3. Ashir Ahmed
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  4. Abu Raihan M. Kamal
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  5. Hua Wang
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  6. Anwaar Ulhaq
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Corresponding author

Correspondence to Md. Rafiqul Islam.

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Islam, M.R., Kabir, M.A., Ahmed, A. et al. Depression detection from social network data using machine learning techniques. Health Inf Sci Syst 6, 8 (2018). https://doi.org/10.1007/s13755-018-0046-0

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