Mining the impact of social media information on public green consumption attitudes: a framework based on ELM and text data mining

This study investigates public attitudes toward green consumption using social media data, exploring reasons for low adoption. It employs the Elaboration Likelihood Model (ELM) and text data mining to analyze how information strategies from government, businesses, and media influence consumer attitudes. Findings reveal that women and those in economically developed regions show greater concern for green consumption. Positive responses are observed toward government policies and corporate actions, but negative responses to media campaigns. Engagement with information and emotional responses significantly influence attitudes. The study provides strategies for policymakers and businesses to improve consumer attitudes and behaviors toward green consumption. The innovative methodology combines ELM theory and text data mining to monitor public attitude change, applicable beyond green consumption.

Jun Fan, Lijuan Peng, Tinggui Chen, Guodong Cong