Publication: Detection of depression-positive Thai Facebook users using posts and their usage behavior
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2021
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en
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978-3-030-79757-7
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item.page.harrt.identifier.callno
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Recent Advances in Information and Communication Technology 2021
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77
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87
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Detection of depression-positive Thai Facebook users using posts and their usage behavior
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Abstract
Detecting clinical depression is an important task to find affected patients for effective treatment; especially in an early state with a higher effective treatment. This work proposes a method for automated detecting the possible depression-positive person from Facebook data; which refers to the user’s textual posts and usage behavior. A machine-learning classification then uses the data to create a model to determine features signifying depression-positive users. We consider used words and statistical data of actions made on Facebook platforms; such as the number of posts; comments; and replies a user made daily; along with time and frequency information of these actions. An experiment was conducted to examine the potential and capability of the proposed method. A model from Neural Networks’ behavior data yielded the best result; a 1.0 F1 score. In contrast; the model of text data from Neural Networks acquired the results as 0.88 F1 scores for classification results. From the models; we also obtain a list of significant features indicating a depression-positive state of users as keywords from text data and notable behavior from action data based on the calculated weight from machine learning.