Social media platforms have firmly established themselves as phenomena of interest for Learning Design and Technology (LDT) researchers, and the data accessible from these platforms provide new methodological possibilities. Given the number, diversity, and constant evolution of both social media platforms and research tools, it would be inappropriate to suggest that there is a single correct way to carry out LDT research with social media data. In contrast, this chapter introduces six broad, interconnected steps to social media research: Conducting Ethical Research; Framing the Research; Organizing the Research Process; Collecting Data; Analyzing Data; and Writing, Sharing, and Publicizing Research. Each step is associated with several considerations, careful attention to which will guide researchers toward a particular correct way to accomplish their specific research objectives.

The existing work on teacher-focused Twitter hashtags typically frames each hashtag as a single, unified phenomenon, thereby collapsing or erasing differences between them (and any resulting implications for learning). In this study, we conceived of teacher-focused hashtags as affinity spaces potentially containing subspaces distinguished by synchronous chats and other, asynchronous communication. We used computational methods to explore how participation differed in terms of content, interactions, and portals between these contexts within the #michED hashtag used by Michigan teachers. During the 2015–2016 academic year, #michED saw more non-chat activity than chat activity, and most participants only engaged in one mode of activity or the other. Participation during chats was associated with more replying as well as more socially-, affectively-, and cognitively-related content, suggesting a focus on social interaction. In contrast, non-chat participation was associated with more retweeting, mentioning, hyperlinks, and hashtags, suggesting a focus on content dissemination. These results suggest that different affinity spaces—and different literacy practices—may exist within the same hashtag to support different objectives. Teachers, teacher educators, and researchers should therefore be careful to make these distinctions when considering Twitter as a learning technology for teachers.

In this paper, we document different expressions of Mormon identity and different approaches to Mormon practice within the #ldsconf Twitter hashtag. In particular, we examine #ldsconf during two important events in the recent history of the Church of Jesus Christ of Latter-day Saints: the presidential transition from Thomas Monson to Russell Nelson in January 2018, and the Church’s formal acceptance of Nelson as Church president in April 2018. Our findings suggest that the #ldsconf hashtag allows for more expression of identity than formal Latter-day Saint contexts and that Twitter hashtags afford a ready audience for religious discussion in which no bounds are set on appropriate identity or practice.

Twitter and other social media have assumed important places in many educators’ professional lives by hosting spaces where new kinds of collegial interactions can occur. However, such spaces can also attract unwelcome Twitter traffic that complicates researchers’ attempts to explore and understand educators’ professional social media experiences. In this article, we define various kinds of spam that we have identified in our research on educators’ uses of Twitter. After providing an overview of the concept of spam, we evaluate the advantages and disadvantages of different approaches to addressing the presence of spam in educator-focused Twitter spaces. Then we suggest practical, holistic metrics that can be employed to help identify spam. Through secondary analyses of our past research, we describe the use of such metrics to identify and deal with spam in three specific cases. Finally, we discuss implications of spam and these suggested methods for teacher educators, instructional designers and educational technology researchers.

Although researchers have discovered a great deal about who uses Twitter for educational purposes, what they post about, when they post and why they participate, there has so far been little work to explore where participants in educational Twitter contexts are located. In this paper, we establish a methodological foundation that can support the exploration of geographical issues in educational Twitter research. We surveyed 46 participants in one educational Twitter hashtag, #michED, to determine where they lived; we then compared these responses to results from three digital methods for geolocating Twitter users (human coding, machine coding and GPS coding) to explore these methods’ affordances and constraints. Human coding of Twitter profiles allowed us to analyze more participants with higher levels of accuracy but also has disadvantages compared to other digital—and traditional—methods. We discuss the additional insights obtained through geolocating #michED participants as well as considerations for using geolocation and other digital methods in educational research.