Towards Improving Sample Representativeness of Teachers on Online Social Media: A Case Study on Pinterest

Abstract

Many teachers utilize online social media to supplement their students’ needs and enhance their professional activities, curating millions of educational resources. In fact, during the Coronovirus pandemic, online curation of resources provides teachers a repository of materials to provide students in online space. Teachers’ engagement online then provides the ability to learn more about how teachers are addressing students’ learning needs and potentially improve the quality of the resources they share. Historically, to perform such a study, we often survey some teachers and then leverage their shared resources to investigate education-related research questions. However, this can lead to problems including sample representativeness where surveyed teachers may not be representative of the population of teachers in social media. In this paper, we attempt to improve the sample representativeness of teachers on Pinterest. We first survey 541 teachers in the United States as seed samples and then collect their online data and social connections on Pinterest. Then, we devise a heuristic that automatically identifies other Pinterest accounts that are likely to be teachers thus improving the sample representativeness. Finally, we evaluate our heuristic with advanced machine learning techniques.