Background/Context: Teachers and educational professionals can draw on (informal) networks to foster their professional development. Moreover, a growing number of studies have shown that teachers use social networking sites (SNSs), such as Twitter, to keep up to date with the latest news on education and share resources with colleagues. Additionally, social capital can help to explain potential benefits of networking and has already been used to better understand professional development. Purpose/Objective/Research Question/Focus of Study: The aim of this study is to contribute to a better understanding of the informal networking of educators in SNSs. To achieve this goal, we first indicate how the concept of social capital can be used to assess communication flows within SNSs. Then, we consider social networking metrics and question whether they are as relevant in an online realm. Next, we argue for an adjusted brokerage index, namely the social brokerage index (SBI), which can help to shed light on how brokerage positions are shaped by different people within SNSs. Finally, we provide empirical data from six educational hashtag conversations on Twitter to test the relevance and applicability of the SBI. Research Design: Using Twitter data from six (international) hashtag conversations between teachers and educational professionals, we apply social network analysis methods to assess the potential formation of social capital. In applying this method to the Twitter conversations in question, we first collected data on the Twitter users who contributed to the applicable hashtag conversation. Subsequently, we built directed unweighted one-mode networks based on mentions, and replies matrices. Second, we computed the in-degree, out-degree, and overall degree centrality metrics of all users (nodes) taking part in the applicable hashtag discussions. Additionally, we also determined users' brokerage positions, which is another indicator for social capital formation within networks. Questioning the relevance of these metrics in the context of SNSs, we propose the SBI, which departs from previous work that has largely been framed by considerations around general account characteristics (follower/following ratio), general communication patterns (retweet/mention ratio), or in-degree metrics. Conclusions/Recommendations: Based on our findings, we believe that our proposed SBI has added value to the analyses of network behavior beyond the scope of Twitter. More specifically, the SBI could help to understand what type of discussions draw what type of participants and thereby shed more light on how SNSs contribute to social capital formation among teachers and educational professionals.