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Accepted for/Published in: JMIR Infodemiology

Date Submitted: Apr 16, 2023
Date Accepted: Apr 21, 2025

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

Messaging and Information in Mental Health Communication on Social Media: Computational and Quantitative Analysis

Ivic-Britt RK, Carmack H, Kanthawala S, Ritchart A

Messaging and Information in Mental Health Communication on Social Media: Computational and Quantitative Analysis

JMIR Infodemiology 2025;5:e48230

DOI: 10.2196/48230

PMID: 40608453

PMCID: 12244273

Messaging and Information in Mental Health Communication on Social Media

  • Rebecca Katherine Ivic-Britt; 
  • Heather Carmack; 
  • Shaheen Kanthawala; 
  • Amy Ritchart

ABSTRACT

Background:

Mental health organizations have the vital and difficult task of shaping public discourse and providing important information. Social media platforms such as X serve as such communication channels and analyzing organizational health information offers valuable insights into their guidance and linguistic patterns, which can enhance communication strategies for health campaigns and interventions. The findings inform strategies to enhance public engagement, trust, and the effectiveness of mental health messaging.

Objective:

This study examines the predominant themes and linguistic characteristics of messages from mental health organizations, focusing on how these messages’ structure information, engage audiences, and contribute to public information and discourse on mental health.

Methods:

A computational content analysis was conducted to identify thematic clusters within messages from 17 unique mental health organizations, totaling 326,967 tweets and approximately 7.2 million words. Additionally, Linguistic Inquiry and Word Count (LIWC) was used to analyze affective, social, and cognitive processes in messages with positive versus negative sentiment. Differences in sentiment were assessed using a Mann-Whitney U test.

Results:

The analysis revealed that organizations predominantly emphasize themes related to community, well-being, and workplace mental health. Sentiment analysis indicated significant differences in affect (P < .0001), social processes (P < .0001), and cognitive processing (P < .001) between positive and negative messages, with effect sizes that were small to medium. Notably, while messages frequently conveyed positive sentiment and social engagement, there was a lower emphasis on cognitive processing, suggesting that more complex discussions about mental health challenges may be underrepresented.

Conclusions:

Organizations use social media to promote engagement and support, often through positively valanced messages. Yet the limited emphasis on cognitive processing may indicate a gap in how organizations address more nuanced or complex mental health issues. Findings demonstrate the need for communication strategies that balance information with depth and clarity, ensuring that messages are trustworthy, actionable, and responsive to multiple mental health needs. By refining digital messaging strategies, organizations can enhance the effectiveness of health communication and improve engagement with mental health resources.


 Citation

Please cite as:

Ivic-Britt RK, Carmack H, Kanthawala S, Ritchart A

Messaging and Information in Mental Health Communication on Social Media: Computational and Quantitative Analysis

JMIR Infodemiology 2025;5:e48230

DOI: 10.2196/48230

PMID: 40608453

PMCID: 12244273

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© 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.