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A good deal of Twitter research focuses on event-detection using algorithms that rely on keywords and tweet density. We present an alternative analysis of tweets, filtering by hashtags related to the 2012 Superbowl and validated against the 2013 baseball World Series. We analyze low-volume, topically similar tweets which reference specific plays (sub-contexts) within the game at the time they occur. These communications are not explicitly linked; they pivot on keywords and do not correlate with spikes in tweets-per-minute. Such phenomena are not readily identified by current event-detection algorithms, which rely on volume to drive the analytic engine. We propose to demonstrate the effectiveness of empirically and theoretically informed approaches and use qualitative analysis and theory to inform the design of future event-detection algorithms. Specifically, we propose theories of Information Grounds and "third places" to explain sub-contexts that emerge. Conceptualizing sub-contexts as a socio-technical place advances the framing of Twitter event-detection from principally computational to deeply contextual.

PurposeThis paper has two purposes. First, to provide insight into the formation of completely online small groups, paying special attention to how their work practices develop, and how they form identity. Second, to pursue conceptual development of a more multi‐level view of completely online group experience, which can be made visible through analysis of the unique interaction logging system used in this study.Design/methodology/approachThe authors conduct a mixed methods study that integrates interviews, grounded theory analysis, case study methods and social network analysis to build a multi‐layered view of completely online group and community development.FindingsCompletely online group formation is explicated as a socio‐technical system. The paper identifies themes of tool uptake and use, and patterns of interaction that accompany group formation and development of completely online group practices. These patterns show little respect for the boundaries of space and time. It then shows how groups who are paired together for two non‐sequential activities develop a common internal structural arrangement in the second activity, and are viewable as groups in the larger course context in four of six cases.Research limitations/implicationsThe time bounded nature of the group and community, combined with the educational context limit the generalizability of these findings.Practical implicationsThe study shows how completely online group development can be made visible. Managers of work teams and teachers who work with classrooms in completely online contexts need to recognize the dynamic structure and interaction practices of completely online teams.Originality/valueFirst, little research has been conducted on completely online group formation. Second, a conceptual understanding of how group members relate to one another and how groups interact with other groups in the same socio‐technical context is not explored in prior work. Third, the paper performs this analysis including data from rich, contextualized usage logs, which enables greater insight into online group interactivity than prior research.

The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as "data factoring" emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing. The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools. Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com.