Deep canvassing with automated conversational agents: Personalized messaging to change attitudes

We test a social media conversational agent for canvassing on the topic of anti-transgender prejudice, replicating and benchmarking treatment effects. In-person deep canvassing is the gold standard for durably changing attitudes on polarizing topics. However, door-to-door canvassing is costly, and many populations may not be feasibly reached in this manner. Campaigns are already conducting outreach using digital tools, including text messages and social media. If appropriately trained agents messaging over social media can achieve a fraction of the effect of in-person canvassing, canvassing may be scaled up to achieve large overall impacts at lower costs. Scripts used in this application are based on those used by transgender allies in the original study. To personalize messaging, the conversational agent uses natural language processing to detect conversational topics, and shares relevant pre-scripted messages of information and third-person experiences, encouraging respondents to engage in perspective-taking with respect to an outgroup. This study demonstrates the potential of automated social media messaging for deep canvassing, with possible applications by governments, public health agencies, and political organizations. Estimated effects are positive and significant under covariate adjustment and reweighting; due to important differential attrition, partial-identification bounds are also reported and include zero.