On the State of Social Media Data for Mental Health Research

Harrigian, Keith; Aguirre, Carlos; Dredze, Mark

doi:10.48550/arxiv.2011.05233

Cited by 5 publications

(8 citation statements)

References 22 publications

(25 reference statements)

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“…We select the task of depression inference for this study, as it is the most widely studied mental health condition in social media research (Harrigian et al, 2020). We consider two Twitter datasets: CLPSYCH (Coppersmith et al, 2015b) and MUL-TITASK (Benton et al, 2017b).…”

Section: Datasetsmentioning

confidence: 99%

“…In an attempt to preemptively address population biases, Amir et al (2019) proposed a cohort-based sampling approach to collect representative measures of wellness amongst the general population. However, as noted in a recent literature reviews (Chancellor and De Choudhury, 2020;Harrigian et al, 2020), no previous computational mental-health study has accounted for differences in population-level depression rates nor explored performance variations across demographic subgroups at training time. Therefore, little is known about the fairness of these automated systems.…”

Section: Introductionmentioning

confidence: 99%

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Gender and Racial Fairness in Depression Research using Social Media

Aguirre¹,

Harrigian²,

Dredze³

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status. The widespread availability of such data has spurred interest in mental health research from a computational lens. While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups. We find that model performance systematically differs for underrepresented groups and that these discrepancies cannot be fully explained by trivial data representation issues. Our study concludes with recommendations on how to avoid these biases in future research.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gender and Racial Fairness in Depression Research using Social Media

Aguirre¹,

Harrigian²,

Dredze³

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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“…Thus, some previous work focused on the counseling part in the online mental health communities (forums), such as TeenHelp (Franz et al, 2020), TalkLife (Sharma et al, 2020). In Chinese domain, Wang et al (2020) From the perspective of the mental health domains, most of the prior work is focusing on singledomain like depression, suicidal ideation, and eating disorders (Harrigian et al, 2020). Instead, PsyQA contains all sorts of general mental health disorders, concerning nine topics labeled by helpseekers including self-growth, emotion, love problem, relationships, behaviors, family, treatment, marriage, and career.…”

Section: Text-based Mental Health-related Datasetsmentioning

confidence: 99%

“…There are some datasets for mental health detection and therapy. However, most of them are collected from general social networking sites such as Twitter, Reddit, and Weibo (Harrigian et al, 2020). General social networking sites contain irrelevant posts or unprofessional responses, which might put NLP systems trained on these corpora at huge risk.…”

Section: Text-based Mental Health-related Datasetsmentioning

confidence: 99%

PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support

Sun¹,

Lin²,

Zheng³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Great research interests have been attracted to devise AI services that are able to provide mental health support. However, the lack of corpora is a main obstacle to this research, particularly in Chinese language. In this paper, we propose PsyQA, a Chinese dataset of psychological health support in the form of question and answer pair. PsyQA is crawled from a Chinese mental health service platform, and contains 22K questions and 56K long and wellstructured answers. Based on the psychological counseling theories, we annotate a portion of answer texts with typical strategies for providing support, and further present in-depth analysis of both lexical features and strategy patterns in the counseling answers. We also evaluate the performance of generating counseling answers with the generative pretrained models. Results show that utilizing strategies enhances the fluency and helpfulness of generated answers, but there is still a large space for future research.

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“…Recent work has mainly focused on delivering cognitive support through conversational agents adopting Cognitive Behavioral Therapy (CBT) principles and has demonstrated the efficacy of such interventions in reducing users’ mental distress, mainly depression and anxiety ( 14 , 24 – 26 ). While most of the existing research on this topic is in English ( 18 , 27 , 28 ), there have been attempts to create Chinese chatbots for CBT ( 26 , 29 – 31 ), demonstrating the importance of employing such systems in China.…”

Section: Introductionmentioning

confidence: 99%

A chatbot for mental health support: exploring the impact of Emohaa on reducing mental distress in China

Sabour

Zhang

Xiao³

et al. 2023

Front. Digit. Health

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IntroductionThe growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availability and reduce the relative costs of mental health support. The provided support can be divided into two main types: cognitive and emotional. Existing work on this topic mainly focuses on constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles. Such agents operate based on pre-defined templates and exercises to provide cognitive support. However, research on emotional support using such agents is limited. In addition, most of the constructed agents operate in English, highlighting the importance of conducting such studies in China. To this end, we introduce Emohaa, a conversational agent that provides cognitive support through CBT-Bot exercises and guided conversations. It also emotionally supports users through ES-Bot, enabling them to vent their emotional problems. In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.Methods and ResultsFollowing the RCT design, the current study randomly assigned participants into three groups: Emohaa (CBT-Bot), Emohaa (Full), and control. With both Intention-To-Treat (N=247) and PerProtocol (N=134) analyses, the results demonstrated that compared to the control group, participants who used two types of Emohaa experienced considerably more significant improvements in symptoms of mental distress, including depression (F[2,244]=6.26, p=0.002), negative affect (F[2,244]=6.09, p=0.003), and insomnia (F[2,244]=3.69, p=0.026).DiscussionBased on the obtained results and participants’ satisfaction with the platform, we concluded that Emohaa is a practical and effective tool for reducing mental distress.

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On the State of Social Media Data for Mental Health Research

Cited by 5 publications

References 22 publications

Gender and Racial Fairness in Depression Research using Social Media

Gender and Racial Fairness in Depression Research using Social Media

PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support

A chatbot for mental health support: exploring the impact of Emohaa on reducing mental distress in China

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