Abstract
This paper presents a multipronged approach to predict early risk of mental health issues from user-generated content in social media. Supervised learning and information retrieval methods are used to estimate the risk of depression for a user given the content of its posts in reddit. The approach presented here was evaluated on the CLEF eRisk 2017 pilot task. We describe the details of five systems submitted to the task, and compare their performance. The comparisons show that combining information retrieval and machine learning methods gives the best results.
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Obtained from a conceptual feature map in SenticNet [5].
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Obtained from Wikipedia pages for “depression”, “psychoactive drugs”, and “list of antidepressants”.
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Briand, A., Almeida, H., Meurs, MJ. (2018). Analysis of Social Media Posts for Early Detection of Mental Health Conditions. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_11
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