Twitter bot surveys: A discrete choice experiment to increase response rates

Abstract

This paper presents a new methodology---the Twitter bot survey---that bridges the gap between social media research and web surveys. The methodology uses the Twitter APIs to identify a target population and then uses the API to deliver a question in the form of a regular Tweet. We hypothesized that this method would yield high response rates because users are posed a question within the social media platform and are not asked, as is the case with most web surveys, to follow a link away to a third party. To evaluate the response rate and identify the most effective mechanism for increasing it, we conducted a discrete choice experiment that evaluated three factors: question type, the use of an egoistic appeal, and the presence of contextual information. We found that, similar to traditional web surveys, multiple choice questions, egoistic appeals, and contextual information all contributed to higher response rates. Question variants that combined all three yielded a 40.0% response rate, thereby outperforming most other web surveys and demonstrating the promise of this new methodology. The approach can be extended to any other social media platforms where users typically interact with one another. The approach also offers the opportunity to bring together the advantages of social media research using APIs with the richness of information that can be collected from surveys.