Cross-cultural differences in language markers of depression online

Loveys, Kate; Torrez, J. Estrella; Fine, Alex B.; Moriarty, Glen; Coppersmith, Glen

doi:10.18653/v1/w18-0608

Cited by 37 publications

(34 citation statements)

References 27 publications

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“…Second, PiaP considered the cultural expression of depression in text analysis in the creation of its English-Tagalog lexicon. This includes the mixed usage of Tagalog and English (Taglish), textolog (shortening of words), emoticons, and emojis, thus allowing for the recognition of “possible cultural variations in the expression of depressive symptoms via electronic data” [63,64] and providing a more nuanced screening. Compared with BinDhim et al [65], although they proved the feasibility of using a mobile app for depression screening by utilizing an app that was an electronic version of the Patient Health Questionnaire (PHQ)-9, they did not use text analysis.…”

Section: Discussionmentioning

confidence: 99%

Validation of an mHealth App for Depression Screening and Monitoring (Psychologist in a Pocket): Correlational Study and Concurrence Analysis

Ramos

Cheng²,

Jonas

2019

JMIR Mhealth Uhealth

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BackgroundMobile health (mHealth) is a fast-growing professional sector. As of 2016, there were more than 259,000 mHealth apps available internationally. Although mHealth apps are growing in acceptance, relatively little attention and limited efforts have been invested to establish their scientific integrity through statistical validation. This paper presents the external validation of Psychologist in a Pocket (PiaP), an Android-based mental mHealth app which supports traditional approaches in depression screening and monitoring through the analysis of electronic text inputs in communication apps.ObjectiveThe main objectives of the study were (1) to externally validate the construct of the depression lexicon of PiaP with standardized psychological paper-and-pencil tools and (2) to determine the comparability of PiaP, a new depression measure, with a psychological gold standard in identifying depression.MethodsCollege participants downloaded PiaP for a 2-week administration. Afterward, they were asked to complete 4 psychological depression instruments. Furthermore, 1-week and 2-week PiaP total scores (PTS) were correlated with (1) Beck Depression Index (BDI)-II and Center for Epidemiological Studies–Depression (CES-D) Scale for congruent construct validation, (2) Affect Balance Scale (ABS)–Negative Affect for convergent construct validation, and (3) Satisfaction With Life Scale (SWLS) and ABS–Positive Affect for divergent construct validation. In addition, concordance analysis between PiaP and BDI-II was performed.ResultsOn the basis of the Pearson product-moment correlation, significant positive correlations exist between (1) 1-week PTS and CES-D Scale, (2) 2-week PTS and BDI-II, and (3) PiaP 2-week PTS and SWLS. Concordance analysis (Bland-Altman plot and analysis) suggested that PiaP’s approach to depression screening is comparable with the gold standard (BDI-II).ConclusionsThe evaluation of mental health has historically relied on subjective measurements. With the integration of novel approaches using mobile technology (and, by extension, mHealth apps) in mental health care, the validation process becomes more compelling to ensure their accuracy and credibility. This study suggests that PiaP’s approach to depression screening by analyzing electronic data is comparable with traditional and well-established depression instruments and can be used to augment the process of measuring depression symptoms.

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Section: Discussionmentioning

confidence: 99%

Validation of an mHealth App for Depression Screening and Monitoring (Psychologist in a Pocket): Correlational Study and Concurrence Analysis

Ramos

Cheng²,

Jonas

2019

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

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“…This last criteria also inherently excludes datasets that lack annotation of mental health status altogether (e.g. data dumps of online mental health support platforms and text-message counseling services) (Loveys et al, 2018;Demasi et al, 2019).…”

Section: Selection Criteriamentioning

confidence: 99%

On the State of Social Media Data for Mental Health Research

Harrigian¹,

Aguirre²,

Dredze³

2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis. 1

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“…Elazar and Goldberg (2018) demonstrated that demographics are implicitly encoded in text data. Wood-Doughty et al (2017) and Loveys et al (2018) both studied differing language use across cultures. The former used a Twitter data set with inferred demographic labels, while the latter used a carefully-curated proprietary data set from 7 Cups of Tea.…”

Section: Related Workmentioning

confidence: 99%

Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models

Sherman¹,

Harrigian²,

Aguirre³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention. For these models to be maximally useful, it is necessary to understand how they perform on various subgroups, especially those defined in terms of protected characteristics. In this paper we study the relationship between user demographicsfocusing on gender -and depression. Considering a population of Reddit users with known genders and depression statuses, we analyze the degree to which depression predictions are subject to biases along gender lines using domaininformed classifiers. We then study our models' parameters to gain qualitative insight into the differences in posting behavior across genders.

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Cross-cultural differences in language markers of depression online

Cited by 37 publications

References 27 publications

Validation of an mHealth App for Depression Screening and Monitoring (Psychologist in a Pocket): Correlational Study and Concurrence Analysis

Validation of an mHealth App for Depression Screening and Monitoring (Psychologist in a Pocket): Correlational Study and Concurrence Analysis

On the State of Social Media Data for Mental Health Research

Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models

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