Differentiating sub-groups of online depression-related communities using textual cues

Thin Nguyen; Bridianne O’Dea; Mark Larsen; Dinh Phung; Svetha Venkatesh; Helen Christensen

doi:10.1007/978-3-319-26187-4_17

Differentiating sub-groups of online depression-related communities using textual cues

Thin Nguyen, Bridianne O’Dea, Mark Larsen, Dinh Phung, Svetha Venkatesh, Helen Christensen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2015
Subtitle of host publication	16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II
Editors	Wojciech Cellary, Dingding Wang, Jianyong Wang, Shu-Ching Chen, Tao Li, Hua Wang, Yanchun Zhang
Place of Publication	Cham, Switzerland
Publisher	Springer Verlag
Pages	216-224
Number of pages	9
ISBN (Electronic)	978-3-319-26187-4
ISBN (Print)	978-3-319-26186-7
DOIs	https://doi.org/10.1007/978-3-319-26187-4_17
Publication status	Published - 2015
Externally published	Yes
Event	16th International Conference on Web Information Systems Engineering, WISE 2015 - Miami, United States Duration: 1 Nov 2015 → 3 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9419
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Web Information Systems Engineering, WISE 2015
Country/Territory	United States
City	Miami
Period	1/11/15 → 3/11/15

Keywords

Feature extraction
Online depression
Textual cues
Web community

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-26187-4_17Licence: In Copyright

https://rdcu.be/dR5EZLicence: In Copyright

Cite this

Nguyen, T., O’Dea, B., Larsen, M., Phung, D., Venkatesh, S., & Christensen, H. (2015). Differentiating sub-groups of online depression-related communities using textual cues. In W. Cellary, D. Wang, J. Wang, S.-C. Chen, T. Li, H. Wang, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2015: 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II (pp. 216-224). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9419). Springer Verlag. https://doi.org/10.1007/978-3-319-26187-4_17

Nguyen, Thin ; O’Dea, Bridianne ; Larsen, Mark et al. / Differentiating sub-groups of online depression-related communities using textual cues. Web Information Systems Engineering – WISE 2015: 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II. editor / Wojciech Cellary ; Dingding Wang ; Jianyong Wang ; Shu-Ching Chen ; Tao Li ; Hua Wang ; Yanchun Zhang. Cham, Switzerland : Springer Verlag, 2015. pp. 216-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9408709ea7e141318379109d9ea6fd3f,

title = "Differentiating sub-groups of online depression-related communities using textual cues",

abstract = "Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.",

keywords = "Feature extraction, Online depression, Textual cues, Web community",

author = "Thin Nguyen and Bridianne O{\textquoteright}Dea and Mark Larsen and Dinh Phung and Svetha Venkatesh and Helen Christensen",

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Nguyen, T, O’Dea, B, Larsen, M, Phung, D, Venkatesh, S & Christensen, H 2015, Differentiating sub-groups of online depression-related communities using textual cues. in W Cellary, D Wang, J Wang, S-C Chen, T Li, H Wang & Y Zhang (eds), Web Information Systems Engineering – WISE 2015: 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9419, Springer Verlag, Cham, Switzerland, pp. 216-224, 16th International Conference on Web Information Systems Engineering, WISE 2015, Miami, United States, 1/11/15. https://doi.org/10.1007/978-3-319-26187-4_17

Differentiating sub-groups of online depression-related communities using textual cues. / Nguyen, Thin; O’Dea, Bridianne; Larsen, Mark et al.
Web Information Systems Engineering – WISE 2015: 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II. ed. / Wojciech Cellary; Dingding Wang; Jianyong Wang; Shu-Ching Chen; Tao Li; Hua Wang; Yanchun Zhang. Cham, Switzerland: Springer Verlag, 2015. p. 216-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9419).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Differentiating sub-groups of online depression-related communities using textual cues

AU - Nguyen, Thin

AU - O’Dea, Bridianne

AU - Larsen, Mark

AU - Phung, Dinh

AU - Venkatesh, Svetha

AU - Christensen, Helen

PY - 2015

Y1 - 2015

N2 - Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.

AB - Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.

KW - Feature extraction

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KW - Textual cues

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DO - 10.1007/978-3-319-26187-4_17

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SN - 978-3-319-26186-7

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 216

EP - 224

BT - Web Information Systems Engineering – WISE 2015

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A2 - Wang, Dingding

A2 - Wang, Jianyong

A2 - Chen, Shu-Ching

A2 - Li, Tao

A2 - Wang, Hua

A2 - Zhang, Yanchun

PB - Springer Verlag

CY - Cham, Switzerland

T2 - 16th International Conference on Web Information Systems Engineering, WISE 2015

Y2 - 1 November 2015 through 3 November 2015

ER -

Nguyen T, O’Dea B, Larsen M, Phung D, Venkatesh S, Christensen H. Differentiating sub-groups of online depression-related communities using textual cues. In Cellary W, Wang D, Wang J, Chen SC, Li T, Wang H, Zhang Y, editors, Web Information Systems Engineering – WISE 2015: 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II. Cham, Switzerland: Springer Verlag. 2015. p. 216-224. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26187-4_17

Differentiating sub-groups of online depression-related communities using textual cues

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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