Exploring the dominant features of social media for depression detection

Abstract

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

“…People with depression fell in loneliness as they do not increase the size of their friends' networks . When compared to others, Depressed people have a smaller friend network [21] as its shown in the previous researches. Akshi Kumara et al [26], a supervised learning-based prediction model, analysed using various extracted features to detect anxious depression disorder.…”

“…LIWC: Linguistic Inquiry and Word Count [5,7,16,21,25] measures the total number of various categories of words used in a text and how can it process texts.…”

“…Supervised learning [26]. Algorithms with labelled [18,21,22] data and defines the variables they want the system to assess for correlations [8]. The input and output of the entire learning algorithm or system is specified.…”

Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

“…With the development of the internet in people’s daily life, people began to share their feelings and problems on social media [ 3 , 4 ] such as Reddit and Twitter. The research of Park et al [ 5 ] showed that people with depression tend to post information about depression and even treatment on social media.…”

“…Hiraga [ 22 ] extracted linguistic features for depression detection, including character n-grams, token n-grams, and lemmas and selected lemmas. Hussain et al [ 3 ] developed an application called the Socially Mediated Patient Portal. The application could generate a series of features for depression detection.…”