Detecting depressed users in online forums

Shrestha, Anu; Spezzano, Francesca

doi:10.1145/3341161.3343511

Cited by 12 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Online forums like Reddit (Tadesse et al 2019a(Tadesse et al , 2019bYates et al 2017 It is often supported by utilising various machine learning approaches to different NLP techniques (Tadesse et al 2019a(Tadesse et al , 2019bZhang et al 2015). Moreover, some studies employed classification algorithms like Decision Trees (Islam et al 2018), Naive Bayes (Biyani et al 2014), and Random Forest (Cacheda et al 2019;Shrestha and Spezzano 2019). With recent advances in neural network models in NLP, a novel contribution for identification of depression has arisen with the implementation of advanced deep learning architectures to outperform more traditional machine learning methods.…”

Section: Forum Data and Depression Identificationmentioning

confidence: 99%

“…Among those assessments, DSM-V criteria are widely used to diagnose clinical depression disorder and have been classified into different severity levels according to their number of symptoms, duration and cause for depressive episodes. Table 1 elaborates Recent work on modelling social media data in Natural language processing (NLP) has focused on distinguishing depressive posts from nondepressive posts (Islam et al 2018;Jeri-Yabar et al 2019;Shrestha and Spezzano 2019). Moreover, limited datasets are available due to the difficulties such as complex nature, time consumption, etc, that exist in the process of data analysing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Dataset for Research on Modelling Depression Severity in Online Forum Data

Arachchige¹,

Jayasuriya²,

Weerasinghe³

2021

Proceedings of the Student Research Workshop Associated With RANLP 2021

View full text Add to dashboard Cite

People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed.Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.

show abstract

Section: Forum Data and Depression Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dataset for Research on Modelling Depression Severity in Online Forum Data

Arachchige¹,

Jayasuriya²,

Weerasinghe³

2021

Proceedings of the Student Research Workshop Associated With RANLP 2021

View full text Add to dashboard Cite

show abstract

“…• LIWC for each user, we create a unique document by concatenating all their posts. Then, we compute the LIWC features of these documents (this is similar to the setting we had in our previous work Shrestha and Spezzano (2019)) and perform the unsupervised anomaly detection task with these features as input.…”

Section: Baselines For Comparisonmentioning

confidence: 99%

Multi-modal social and psycho-linguistic embedding via recurrent neural networks to identify depressed users in online forums

Shrestha

Serra

Spezzano

2020

Netw Model Anal Health Inform Bioinforma

Self Cite

View full text Add to dashboard Cite

Depression is the most common mental illness in the US, with 6.7% of all adults experiencing a major depressive episode. Unfortunately, depression extends to teens and young users as well and researchers have observed an increasing rate in recent years (from 8.7% in 2005 to 11.3% in 2014 in adolescents and from 8.8 to 9.6% in young adults), especially among girls and women. People themselves are a barrier to fighting this disease as they tend to hide their symptoms and do not receive treatments. However, protected by anonymity, they share their sentiments on the Web, looking for help. In this paper, we address the problem of detecting depressed users in online forums. We analyze user behavior in the ReachOut.com online forum, a platform providing a supportive environment for young people to discuss their everyday issues, including depression. We propose an unsupervised technique based on recurrent neural networks and anomaly detection to detect depressed users. We examine the linguistic style of user posts in combination with network-based features modeling how users connect in the forum. Our results on detecting depressed users show that both psycho-linguistic features derived from user posts and network features are good predictors of users facing depression. Moreover, by combining these two sets of features, we can achieve an F1-measure of 0.64 and perform better than baselines.

show abstract

“…As a result, 76-85% of people from those countries are suffering from depression and try to hide their symptoms of depression from others [9]. In terms of demographic factors such as gender, studies have proven more women are affected by depression compared to men [10,11]. Depression has been reported approximately 1.5 times more in women than in men [8].…”

Section: Introductionmentioning

confidence: 99%

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

2021

View full text Add to dashboard Cite

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

show abstract

Detecting depressed users in online forums

Cited by 12 publications

References 12 publications

A Dataset for Research on Modelling Depression Severity in Online Forum Data

A Dataset for Research on Modelling Depression Severity in Online Forum Data

Multi-modal social and psycho-linguistic embedding via recurrent neural networks to identify depressed users in online forums

Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Contact Info

Product

Resources

About