Applying Machine Learning to Detect Depression-Related Texts on Social Networks

Shekerbekova, Shirinkyz; Yerekesheva, Meruyert; Tukenova, Lyailya; Turganbay, Kuralay; Kozhamkulova, Zhazira; Omarov, Batyrkhan

doi:10.1007/978-981-16-3660-8_15

Cited by 1 publication

(1 citation statement)

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“…Once phrases were identified, a manual selection was made to differentiate tweets that could correspond to expressions of sarcasm, song lyrics, etc., and, then, a machine learning algorithm was trained to classify depressive tweets. In Shekerbekova et al [ 34 ], the authors compare different machine learning algorithms to identify posts related to depression. As in our work, the authors selected a set of posts related to depression and general posts.…”

Section: Related Workmentioning

confidence: 99%

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation

Pool-Cen¹,

Carlos

Hernández-Chan

et al. 2023

Healthcare

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Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

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Section: Related Workmentioning

confidence: 99%