Detecting Depression in Social Media Posts Using Machine Learning

Biradar, Abhilash; Totad, Shashikumar G.

doi:10.1007/978-981-13-9187-3_64

Cited by 18 publications

(7 citation statements)

References 8 publications

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“…The outcomes showed that the technique obtained an error rate of 16.54% in DD. Biradar and Totad (2018) Based on this sentiment polarity, the users can be classified as depressive or non-depressive. The technique achieved better results than that of the previously generated DD approaches.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Bhuvaneswari¹,

Prabha²

2023

Expert Systems

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Depression is a severe mental health issue. The user-generated content on social media (SM) is growing nowadays. Some computational approaches have been proposed for detecting depression based on users' SM data. However, because of the use of formal language, short range of words and misspellings in the SM data, depression detection (DD) is a challenging task. This paper proposes a novel deep learning (DL) technique for performing DD of the SM data with the help of the hybrid feature selection (FS) mechanism. Initially, two publicly available datasets containing user tweets are collected for implementing the proposed research model. Then the collected datasets are preprocessed for further processing. The preprocessing phase

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Section: Related Workmentioning

confidence: 99%

“…Biradar and Totad (2018) presented a hybrid framework to carry out the DD of Twitter data. Firstly, SentiStrength was used to generate the score values for the input data.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Bhuvaneswari¹,

Prabha²

2023

Expert Systems

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“…In this phase, the process to develop the mobile application is detailed using the technologies and procedures proposed. The modeling of the Machine Learning software is carried out, in addition [19], at this stage make the choice of the algorithm for learning the artificial intelligence software.…”

Section: Implementation and Developmentmentioning

confidence: 99%

Machine Learning Applied to Prevention and Mental Health Care in Peru

Ponce¹,

Cruz²,

Andrade–Arenas³

2022

IJACSA

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The present research aims to develop an application that allows the early and timely detection of signs of problems in the mental health of citizens. Agile methodology was used, with its SCRUM framework developing its four steps. In addition, technological tools such as artificial intelligence, mobile applications, social networks and the python programming language were used. Also using SQL Server, Android Studio and the Marvel applications, the latter for the design of the prototypes, through the method of sentiment analysis and machine learning, in order to create a mobile application that is as accurate as possible in its results. For this, several types of algorithm were evaluated, managing to select the most appropriate one since it works based on information collected through the social networks Facebook and Twitter. The result that was obtained was the application that uses machine learning to prevent and take care of mental health in Peru, thus benefiting the citizens of society.

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“…Analysis of social media for predicting mental disorders can be done on a post-level basis using explicit or implicit attributes of the post [8]- [10], at the user-level by aggregating multiple posts as a single document or analyzing behavioral changes over time [9], [11]- [16], or finally at populationlevel. For example, [17] developed a probabilistic model to detect the behavioral changes associated with the onset of depression.…”

Section: Related Workmentioning

confidence: 99%

Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire

Skaik

Inkpen

2022

IEEE Access

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The risk for depression and anxiety increased as people adjusted to a new normal after the COVID-19 pandemic. Early detection and appropriate onset treatment and support can reduce the consequences of depression. Automatic detection of depression in social media has recently become an important area of investigation. However, because of the lack of extensive annotated data, we propose a method for using a model that learns to answer a depression questionnaire and apply it to make population-level predictions. We used the eRisk 2021 Task 3 training dataset to build an automated model to fill the Beck's Depression Inventory (BDI) questionnaire. We selected the best performing model for each group of questions based on predefined metrics and consolidated those models into one model (called the BDI_M ulti_M odel). The BDI_M ulti_M odel achieved better performance than the state-of-the-art for this challenging task. Then, we used this model for inference on a Canadian population dataset and compared its predictions with the statistics of the most recent mental health survey conducted by Statistics Canada. The correlation between the inference of the answered questionnaire based on our BDI_M ulti_M odel and the official statistics showed a strong Pearson correlation of 0.90.

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Detecting Depression in Social Media Posts Using Machine Learning

Cited by 18 publications

References 8 publications

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Machine Learning Applied to Prevention and Mental Health Care in Peru

Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire

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