In this work, we perform a large-scale investigation of teacher connections in online social media. To this end, we first construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, as well as thousands of connections they have established. Then, considering some crucial teacher-related attributes (e.g., their states and grade levels), we characterize direct and indirect teacher connections. Through this characterization, we discover that teachers are predominately connected to their peers in the same district or at least within the same state, and seldom there exist links between teachers outside their districts and states. This hinders the proper diffusion of information and many other advantages that a teacher-teacher connection in an online social media can bring about, e.g., getting advice from their peers. To alleviate this problem, we utilize advances in machine learning and propose a link recommendation system suggesting teachers connect with their similar peers on Pinterest. Our system's evaluation reveals that many new teacher-teacher connections are suggested, which leads to a more cohesive network among teachers rather than the existing localized ego networks.
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