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Data Set Creation and Empirical Analysis for Detecting Signs of Depression from Social Media Postings

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Computational Intelligence in Data Science (ICCIDS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 654))

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Abstract

Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. There are many methods and modalities for detecting depression that involves physical examination of the individual. However, diagnosing mental health using their social media data is more effective as it avoids such physical examinations. Also, people express their emotions well in social media, it is desirable to diagnose their mental health using social media data. Though there are many existing systems that detects mental illness of a person by analysing their social media data, detecting the level of depression is also important for further treatment. Thus, in this research, we developed a gold standard data set that detects the levels of depression as ‘not depressed’, ‘moderately depressed’ and ‘severely depressed’ from the social media postings. Traditional learning algorithms were employed on this data set and an empirical analysis was presented in this paper. Data augmentation technique was applied to overcome the data imbalance. Among the several variations that are implemented, the model with Word2Vec vectorizer and Random Forest classifier on augmented data outperforms the other variations with a score of 0.877 for both accuracy and F1 measure.

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Notes

  1. 1.

    https://www.healthline.com/health/depression/effects-on-body.

  2. 2.

    https://www.reddit.com.

  3. 3.

    https://en.wikipedia.org/wiki/Interrater_reliability.

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Acknowledgements

We would like to thank the Department of Science and Technology - Science and Engineering Research Board (DST-SERB) for providing funds to annotate the collected data.

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Authors and Affiliations

  1. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Kayalvizhi Sampath & Thenmozhi Durairaj

Authors
  1. Kayalvizhi Sampath
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  2. Thenmozhi Durairaj
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Correspondence to Kayalvizhi Sampath .

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Editors and Affiliations

  1. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Lekshmi Kalinathan

  2. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Priyadharsini R.

  3. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Madheswari Kanmani

  4. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Manisha S.

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Sampath, K., Durairaj, T. (2022). Data Set Creation and Empirical Analysis for Detecting Signs of Depression from Social Media Postings. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_11

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  • DOI: https://doi.org/10.1007/978-3-031-16364-7_11

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