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

Juan Pablo Alperin, Erik Warren Hanson, Kenneth Shores, Stefanie Haustein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publication8th International Conference on Social Media and Society
Subtitle of host publicationSocial Media for Good or Evil, #SMSociety 2017
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450348478
StatePublished - Jul 28 2017
Event8th International International Conference on Social Media and Society, #SMSociety 2017 - Toronto, Canada
Duration: Jul 28 2017Jul 30 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F129683


Other8th International International Conference on Social Media and Society, #SMSociety 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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