Deep Temporal Modelling of Clinical Depression through Social Media Text
- URL: http://arxiv.org/abs/2211.07717v3
- Date: Thu, 30 Mar 2023 15:03:17 GMT
- Title: Deep Temporal Modelling of Clinical Depression through Social Media Text
- Authors: Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan and Osmar R.
Za\"iane
- Abstract summary: We develop a model to detect user-level clinical depression based on a user's temporal social media posts.
Our model uses a Depression Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms.
- Score: 1.513693945164213
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We describe the development of a model to detect user-level clinical
depression based on a user's temporal social media posts. Our model uses a
Depression Symptoms Detection (DSD) classifier, which is trained on the largest
existing samples of clinician annotated tweets for clinical depression
symptoms. We subsequently use our DSD model to extract clinically relevant
features, e.g., depression scores and their consequent temporal patterns, as
well as user posting activity patterns, e.g., quantifying their ``no activity''
or ``silence.'' Furthermore, to evaluate the efficacy of these extracted
features, we create three kinds of datasets including a test dataset, from two
existing well-known benchmark datasets for user-level depression detection. We
then provide accuracy measures based on single features, baseline features and
feature ablation tests, at several different levels of temporal granularity.
The relevant data distributions and clinical depression detection related
settings can be exploited to draw a complete picture of the impact of different
features across our created datasets. Finally, we show that, in general, only
semantic oriented representation models perform well. However, clinical
features may enhance overall performance provided that the training and testing
distribution is similar, and there is more data in a user's timeline. The
consequence is that the predictive capability of depression scores increase
significantly while used in a more sensitive clinical depression detection
settings.
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