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Currently accepted at: JMIR Mental Health

Date Submitted: Nov 29, 2017
Open Peer Review Period: Nov 30, 2017 - Jan 31, 2018
Date Accepted: Jul 24, 2018
(closed for review but you can still tweet)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/mental.9533

The final accepted version (not copyedited yet) is in this tab.

The final, peer-reviewed published version of this preprint can be found here:

Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

DeJohn AD, Schulz EE, Pearson AL, Lachmar EM, Wittenborn AK

Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

JMIR Ment Health 2018;5(4):e61

DOI: 10.2196/mental.9533

PMID: 30401662

PMCID: 6246977

Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

  • Amber D. DeJohn; 
  • Emily English Schulz; 
  • Amber L Pearson; 
  • E Megan Lachmar; 
  • Andrea K Wittenborn

ABSTRACT

Background:

Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression.

Objective:

This study aimed to identify communities where Twitter users tweeted using the hashtag #MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression.

Methods:

Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation.

Results:

We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting.

Conclusions:

Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings.


 Citation

Please cite as:

DeJohn AD, Schulz EE, Pearson AL, Lachmar EM, Wittenborn AK

Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

JMIR Mental Health. (forthcoming/in press)

DOI: 10.2196/mental.9533

URL: https://preprints.jmir.org/preprint/9533

PMID: 30401662

PMCID: 6246977

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.