CLPsych 2016 Shared Task: Triaging content in online peer-support forums

Milne, David; Pink, Glen; Hachey, Ben; Calvo, Rafael A.

doi:10.18653/v1/w16-0312

Cited by 72 publications

(87 citation statements)

References 25 publications

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“…Homan et al (2014) and O'Dea et al (2015) detect posts containing suicide ideation and distress, and Li et al (2015) investigate unhelpful, stigmatizing reactions to suicide on the Chinese social media platform Weibo. Milne et al (2016) host a shared task for identifying and prioritizing concerning content on ReachOut.com's peer support forum.…”

Section: Nlp In Mental Health Applications 651 2 Methods and Overviewmentioning

confidence: 99%

“…When the communities grow this becomes a challenge. The CLPsych 2016 shared task (Milne et al 2016) aimed to address such challenges of scale by allowing moderators to focus their efforts where it is most needed. It collected forum posts from ReachOut.com -a site for young Australians facing tough times -and asked participants to automatically triage them as green (no intervention required), amber (a moderator should ideally respond, but not urgently), red (a moderator should respond as soon as they can) or crisis (the post indicates someone is at risk of harm).…”

Section: Online Forums and Support Groupsmentioning

confidence: 99%

See 1 more Smart Citation

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

Self Cite

237

148

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Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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Section: Nlp In Mental Health Applications 651 2 Methods and Overviewmentioning

confidence: 99%

Section: Online Forums and Support Groupsmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

Self Cite

237

148

View full text Add to dashboard Cite

show abstract

“…Through analysis of tweets posted by individuals attempting committing suicide, [19] indicate quantifiable signals of suicidal ideations. Moreover, the 2016 ACL Computational Linguistics and Clinical Psychology Workshop [20] defined a shared task on detecting the severity of the mental health forum posts. All of these studies define some discriminative features to classify depression in user-generated content in a message, for a user or at a community level.…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Yazdavar

Al-Olimat

Ebrahimi

et al. 2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

127

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With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

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“…Extracting features that best represent the social media text is a key step of such systems. A broad array of language representation and classification methods have been applied to triage on posts from the Australian mental health forum Reachout.com , which provides mental health information and support for youth online [14,15] . Recently, transfer learning has been used for text classification across a variety of contexts.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study

et al. 2020

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BackgroundMental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data which can be mined to predict mental health states using machine learning methods. ObjectiveWe benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools to triage content for moderator attention. MethodsWe used 1588 labeled posts from the CLPsych 2017 shared task collected from the Reachout.com forum (Milne et al., 2019). Posts were represented using lexicon based tools including VADER, Empath, LIWC and also used pre-trained artificial neural network models including DeepMoji, Universal Sentence Encoder, and GPT-1. We used TPOT and auto-sklearn as AutoML tools to generate classifiers to triage the posts. ResultsThe top-performing system used features derived from the GPT-1 model, which was finetuned on over 150,000 unlabeled posts from Reachout.com . Our top system had a macro averaged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from meta-data or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. We additionally present visualizations that aid understanding of the learned classifiers. ConclusionsWe show that transfer learning is an effective strategy for predicting risk with relatively little labeled data. We note that finetuning of pretrained language models provides further gains when large amounts of unlabeled text is available.

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CLPsych 2016 Shared Task: Triaging content in online peer-support forums

Cited by 72 publications

References 25 publications

Natural language processing in mental health applications using non-clinical texts

Natural language processing in mental health applications using non-clinical texts

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study

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