Environmental Advocacy Messages: Relationships Between the Messages that Constituents Send to Decision Makers and Organizational Engagement

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Environmental advocacy organizations aim to help citizens contact their policymakers, to recruit new members, and to increase their contacts’ level of engagement with organization issues. They use online petitions and form-letter services for these purposes. These services put citizens in contact with policymakers and encourage citizens to take follow-up actions, such as sending another message, referring a friend, or making a donation. While these services effectively recruit members, they marginally influence policymakers. To increase influence, organizations now ask petitioners to include personal messages in their communications. This dissertation asks if text analysis of these personal messages can help advocacy organizations further fulfill their recruitment and engagement goals. It investigates text-metrics both for predicting engagement from existing contacts and for services, such as chatbots, to suggest follow-up actions to new contacts. Methods employ rule-based text analysis tools (LIWC, VADER, Flesch Reading Ease, and Regular Expressions) to pilot the use of pronouns, sentiment, writing complexity, and the identification of personal stories as predictors of engagement. Data include over two million messages and nearly 500,000 personal messages from over 150,000 individuals supporting sustainable policies and projects. Results reveal relationships between messages and two engagement factors: (1) the number of messages that groups of contacts send and (2) payment of membership dues. Results also bolster research that highlights the importance of identifying contacts who can share stories about how environmental issues have affected them. Conclusions encourage advocacy organizations and policymakers to analyze messages to increase engagement and understand constituency support of policies and projects. Future work may integrate text analysis into membership models and advocacy services. Future work may also improve personal story classification and investigate machine-learning for identifying potential members.