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.
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Hemtanon, S., Aekwarangkoon, S., Kittiphattanabawon, N. (2021). Detection of Depression-Positive Thai Facebook Users Using Posts and Their Usage Behavior. In: Meesad, P., Sodsee, D.S., Jitsakul, W., Tangwannawit, S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_8
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