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Bots are widespread in a variety of online platforms including online communities. Bots perform various useful tasks but little is known about how bots may alter how humans interact with each other and exchange information. We draw on social exchange theory and symbolic interactionism to highlight the social aspect of human cognition and action and theorize how bots affect the online social environment and subsequently change how people interact with each other. We examine the effects of two fundamental forms of bots: reflexive and supervisory, and their differential impact on social exchange. With data from discussion communities on Reddit, we show that reflexive bots increase unidirectional exchange, whereas supervisory bots reduce centralization. Further, reflexive bots increase the depth of discourse, whereas supervisory bots reduce the breadth of discourse. We discuss the implications of these findings on theorizing about bots and the design of online communities. [ABSTRACT FROM AUTHOR]

How can formal organizations and open communities engage in mutually beneficial relationships? We develop a framework that explains how an open community's engagement with a formal organization can be mutually beneficial to both in the longterm. We theorize how a symbiotic relationship between open communities and formal organizations can be forged by nurturing various forms of capital flow between them as well as creating virtuous feedback loops within and across each other's capital creation systems. The framework has implications for understanding innovation in ecosystems and networked economies. [ABSTRACT FROM AUTHOR]

Extant literature emphasizes the fit between tasks and technology features to impact performance. Specialized information systems such as Enterprise Collaboration Systems (ECSs) are designed to align with the tasks of collaboration, coordination, and communication in organizations. Despite this fit, users often continue to use generic default technologies (i.e., general purpose software such as an Excel spreadsheet) to perform the tasks that are supported by the specialized systems. We investigate this phenomenon using a multiple case study approach. We present our initial findings and elaborate on the emerging theme of remixing of generic defaults with specialized software as a new perspective in exploring this phenomenon. [ABSTRACT FROM AUTHOR]

Online communities bring together members of common interest and are recognized as generators of significant knowledge. In healthcare, online communities are a medium for patients to share their stories, gain peer support, and learn more about medical conditions. To succeed, these communities need to sustain their knowledge flows and resolve arising tensions. The current literature suggests that participation dynamics and emergent organizational structure are sufficient to resolve tensions in online communities. Based on increased evidence of the need of formal management to sustain online communities, we propose that emergent structure and roles may not always be sufficient when tensions are prevalent and stakes are high such as in online patient communities. We conduct an inductive field study of Mayo Clinic Connect, a leading online patient community. The findings suggest that formal management and emergent leadership play a complementary role to resolve tensions and sustain knowledge flows. We theorize a virtuous cycle of moderation and socialization that effectively integrates the effort of the community formal management and emergent leaders. [ABSTRACT FROM AUTHOR]

The article presents a research study on the use of microblogging, a social media application, to disseminate information about the Gulf of Mexico oil spill in 2010 and how it differs from social media optimizations (SMOs) and news media outlets. The study found that microblogging can afford discourse that can be polemical and social media such as microblogging can provide a platform to grassroots actors that would allow them to express their opinions publicly. It revealed that the advent of social media allows people to express, publicize and circulate their thoughts.

Open online communities have become widespread forms of organizing in which multiple participants share knowledge, and communicate on the basis of specific commonalities. Yet, we still know little about their genesis. Given the importance of these new forms of organizing, it is crucial to understand their formation. How do they come to be? This study examines how multiple, heterogeneous participants converge around what they shape as shared interests. It engages in computationally- intensive theory development by examining the emergence of the data science open online community on the social media platform Twitter. It builds theory on the genesis of an open online community as a process of spinning and twisting of discourses that coalesce the interests of the members. This study contributes to scholarship on online communities by theorizing the process by which they come to exist as well as the literature on organizational dialog by elaborating on how dialog leads to self-organization in open communities. [ABSTRACT FROM AUTHOR]

Today's world is characterized with an ever-increasing influx of data generated at an unparalleled scale. Big data provide unprecedented opportunities for social scientists to apply computational techniques to understand patterns of individual and group behavior that used to be beyond the reach of scientific method. At the same time, big data pose challenges for exploration and inductive understanding of social phenomena. This is in particular more prevalent when questions are not well defined and data is unstructured, making it less amenable to quantification and computation. In this conceptual paper, we review some of the key challenges that big data raise for qualitative researchers trained in the grounded theory methods (GTM) tradition, and then outline guiding principles for designing a system to respond to these challenges. Paying attention to the particularities of qualitative data analysis, we suggest several design patterns suiting the tasks involved and matching them to challenges of applying GTM to big data sets. We are in the process of developing a system which, as an initial prototype, incorporates these design patterns and addresses the aforementioned key challenges. [ABSTRACT FROM AUTHOR]

This paper proposes a new mechanism that explains how new members in online communities establish ties with existing members. Contrary to mainstream tie formation mechanisms (e.g. preferential attachment and small world clustering) that rely solely on structural properties to predict new relationships, we propose that members' interests represented by the content of messages exchanged among members are a key factor in forming future relationships. Using statistical language modeling, we show that a new member chooses to link to other existing members based on shared interests implied by their previous communication. We validate the model using longitudinal data from an open-source online community in the context of healthcare IT. [ABSTRACT FROM AUTHOR]