Automatically Estimating the Severity of Multiple Symptoms Associated with Depression

Maupomé, Diego; Armstrong, Maxime D.; Belbahar, Raouf Moncef; Alezot, Josselin; Balassiano, Rhon; Rancourt, Fanny; Queudot, Marc; Mosser, Sébastien; Meurs, Marie-Jean

doi:10.1007/978-3-031-04431-1_11

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…We refer the reader to the corresponding shared task surveys for a detailed analysis (Losada et al, 2020;Parapar et al, 2021). In eRisk2020, BioInfo (Trifan et al, 2020) and Relai (Maupomé et al, 2020) methods obtained their own datasets to perform standard ML classifiers using engineered features as linguistic markers. Other deep learning approaches, such as ILab (Castaño et al, 2020) and UPV (Uban and Rosso, 2020), focused their efforts on the use of large language models (LLMs) explicitly trained for depression severity estimation.…”

Section: Discussionmentioning

confidence: 99%

Semantic Similarity Models for Depression Severity Estimation

Pérez,

Warikoo,

Wang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting symptoms severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI-II) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving improvement over state of the art in terms of measuring depression level 1 . * Work done during an internship at UKP Lab. 1 gitlab.irlab.org/anxo.pvila/semantic-4-depression.

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Section: Discussionmentioning

confidence: 99%

Semantic Similarity Models for Depression Severity Estimation

Pérez,

Warikoo,

Wang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…There are many ways in which topic modeling could be performed. For instance, one research team utilized the eRisk 2018 dataset, a cluster of written production from Reddit, a social media platform [8].…”

Section: Topic Modelingmentioning

confidence: 99%

To what extent does machine learning benefit in predicting personal mental issues through social media analysis?

Gao

2024

ACE

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Social media has become an increasingly popular platform for individuals to express their emotions and share personal experiences. This unique characteristic of social media has led to its recognition as a valuable resource for studying mental health trends at the individual level. This article investigates the application of machine learning techniques, such as sentiment analysis, emotion extraction, and topic modeling, in detecting signs of depressive symptoms within social media content. By examining and synthesizing findings from multiple studies, this review provides an analysis of the advantages and limitations of these machine learning techniques in this specific context. Furthermore, it highlights the ethical considerations that should be taken into account in future research endeavors. The integration of machine learning techniques with social media data presents a promising avenue for identifying and addressing mental health concerns, but it is essential to approach this research responsibly and ethically. The growing body of knowledge in this field can contribute to the development of effective interventions and support for individuals experiencing depressive symptoms.

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“…Several researchers extracted single set feature groups such as N-grams (Wongkoblap et al, 2017), Bag-of-Words (Benton et al, 2017;Nadeem, 2016), LIWC (Paul et al, 2018) or LDA (Coppersmith et al, 2015;Maupomé et al, 2018) for diagnosing depression in user postings. Other studies (Resnik et al, 2015;Preoţiuc-Pietro et al, 2015;Nguyen et al, 2014;Schwartz et al, 2014;Tsugawa et al, 2015) compared the effectiveness of each of these distinct characteristics using different machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions

Chatterjee

Kumar²,

Sarkar

2023

International Journal of Intelligent Information Technologies

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The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.

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Automatically Estimating the Severity of Multiple Symptoms Associated with Depression

Cited by 3 publications

References 17 publications

Semantic Similarity Models for Depression Severity Estimation

Semantic Similarity Models for Depression Severity Estimation

To what extent does machine learning benefit in predicting personal mental issues through social media analysis?

Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions

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