Commenting on Brand Posts during a Crisis: The Relationship between Context-Induced Moods and Brand Message Processing across Three Social Media Platforms

Abstract

Within the context of COVID-19, this study examines the relationship between context-induced moods and consumers’ responses to two different types of brand posts on social media: profit-driven posts and public-driven posts. Using both social media data (24,578 user comments on 14 brand posts on Facebook, Instagram, and Twitter) and survey data (356 subjects recruited from Amazon.com’s Mechanical Turk), we found that across all three social media platforms (i.e., Facebook, Instagram, and Twitter), the more negative mood one was in when using a social media platform during the pandemic, the more likely one would leave a comment to praise the company endorser of the public-driven post (comment type 5) as opposed to leaving a comment to discuss serious issues, shedding light on the prediction of negative-state relief model. By contrast, across all three social media platforms, there is no relationship between context-induced moods and types of comments people leave on a profit-driven brand post. We provide theoretical, practical, and methodological implications for future research.

Keywords:

• Ad processing
• context-induced moods
• negative-state relief model
• social media
• user comments

Disclosure statement

No potential conflicts of interest were reported by the author(s).

Notes

1 We selected the fast-food industry because of the focus on the role of context-induced mood. According to the Foote, Cone, and Belding (FCB) grid (Ratchford 1987), fast food belongs to the quadrant of satisfaction (strong feelings and low involvements). The typical purchase behavior in this category is driven by emotions. Therefore, the role of context-induced mood is expected to be salient in this industry.

2 The Python script is available at https://github.com/drawrowfly/instagram-scraper. Note that the script we used might now be outdated because Instagram updates the platform regularly.

Additional information

Funding

This research was financially supported by the University Grants Program from San Diego State University.