Research on Brand Crisis Management Based on Social Media Big Data

Abstract

With the popularity of social media, brand crises often quickly ferment in a very short period of time, with negative emotions and algorithmic recommendation mechanisms overlapping, posing unprecedented challenges to corporate reputation. Traditional crisis management theories are mostly based on static analysis and are difficult to adapt to the non-linear and multimodal evolution of public opinion dissemination characteristics in the social media environment. This article systematically reviews the research on brand crisis management based on social media big data, and constructs a theoretical framework covering the entire chain of monitoring, analysis, response, and governance around four core links: crisis identification and warning, evolution mechanism analysis, response strategy formulation, and reputation repair. The research focuses on the collection and multimodal fusion processing of multi-source heterogeneous data, fine-grained sentiment mining and dynamic threshold warning technology, diffusion path research based on social network analysis, and empirical modification of lifecycle models. The research results indicate that big data technology significantly improves the sensitivity of crisis identification and the adaptability of strategic response, promoting the transformation of crisis management from empirical judgment to data-driven. At the same time, the study also revealed practical challenges such as data barriers, algorithmic biases, and privacy protection. This article promotes the dynamic reconstruction of classic crisis communication theory at the theoretical level and provides methodological references for enterprises to build data-driven crisis resilience systems at the practical level. Future research needs to focus on the catalytic role of generative artificial intelligence in crisis propagation, as well as the application of privacy computing technology in cross platform data collaborative governance.