Abstract
Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.
Highlights
ACCORDING to World Health Organization (WHO) [1], more than 300 million people worldwide are suffering from depression, which equals about 4.4 percent of the global population
The models corresponding to the name of a word embedding refer to a Convolutional Neural Networks (CNN) using this embedding as input vectorization, the models named “Meta LR” refer to the logistic regression based on metadata, and the final four results were obtained by calculating the mean of the metadata probabilities and the neural network output
A second threshold has been reported for the self-trained fastText reddit model and the metadata LR to illustrate to which extent slightly different thresholds can have an effect on ERDEo scores
Summary
ACCORDING to World Health Organization (WHO) [1], more than 300 million people worldwide are suffering from depression, which equals about 4.4 percent of the global population. While forms of depression are more common among females (5.1 percent) than males (3.6 percent) and prevalence differs between regions of the world, it occurs in any age group and is not limited to any specific life situation. Latest results from the 2016 National Survey on Drug Use and Health in the United States [3] report that, during the year 2016, 12.8 percent of adolescents between 12 and Manuscript received 8 Apr. 2018; revised 25 Nov. 2018; accepted 4 Dec. 2018. Date of publication 18 Dec. 2018; date of current version 4 Feb. 2020.
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