Navigating the Storm: The Role of Platform Algorithm in Enhancing Brand Reputation in Social Media Firestorms

Social media firestorms, characterized by the rapid spread of negative electronic word of mouth (eWOM), pose unique challenges and opportunities for brand reputation management. However, there is a gap in understanding the role of platform algorithms and dissonant content in enhancing brand reputation during such crises. This study, underpinned by the selective exposure theory, aims to explore the moderating role of social media platform algorithms (content discovery vs. personalization) on the impact of negative eWOM on brand reputation via the processing of dissonant content—emotional and rational—during social media firestorms. To test our model, we plan to conduct three experimental studies. The findings are expected to contribute theoretically to the literature on social media firestorms and provide strategic insights into leveraging algorithmic dynamics to manage brand perception effectively during these critical events.