Social Media Collaboration in Software Projects

Social media has had a big impact on the way that software projects are managed and the way that stakeholders interact with each other: indeed, the nature of soft-ware projects has evolved substantially in keeping with the evolution of technology. A direct consequence of the ubiquity of the internet is the increasing trend towards cooperation outside the boundaries of an office. The interactions involved in soft-ware projects have changed accordingly and can be broadly divided into two types (1) interactions among stakeholders who are in a single location, (for example people sharing the same office space) and (2) interactions among stakeholders who are in distributed locations (for example software projects which are partly implemented offshore). Social media has been and remains a significant facilitator to these kinds of interactions. This chapter looks at the implications of the use of social media in 21st Century software projects

Facilitating Organisational Fluidity with Computational Social Matching

Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Peer reviewe

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

CrowdLens: Experimenting with Crowd-Powered Recommendation and Explanation

Recommender systems face several challenges, e.g., recommending novel and diverse items and generating helpful explanations. Where algorithms struggle, people may excel. We therefore designed CrowdLens to explore different workflows for incorporating people into the recommendation process. We did an online experiment, finding that: compared to a state-of-the-art algorithm, crowdsourcing workflows produced more diverse and novel recommendations favored by human judges;some crowdworkers produced high-quality explanations for their recommendations, and we created an accurate model for identifying high-quality explanations;volunteers from an online community generally performed better than paid crowdworkers, but appropriate algorithmic support erased this gap. We conclude by reflecting on lessons of our work for those considering a crowdsourcing approach and identifying several fundamental issues for future work

Understanding How People Use Natural Language to Ask for Recommendations: Query Dataset

This dataset describes subjects' initial and follow-up queries from the research paper "Understanding How People Use Natural Language to Ask for Recommendations", published in the ACM Conference on Recommender Systems (RecSys), 2017. The data were collected on movielens.org between May 12 and May 24, 2016.This material is based on work supported by the National Science Foundation under grants IIS-0964695, IIS-1017697, IIS-1111201, IIS- 1210863, and IIS-1218826, and by a grant from Google

Building Member Attachment in Online Communities: Applying Theories of Group Identity and Interpersonal Bonds

Online communities are increasingly important to organizations and the general public, but there is little theoretically based research on what makes some online communities more successful than others. In this article, we apply theory from the field of social psychology to understand how online communities develop member attachment, an important dimension of community success. We implemented and empirically tested two sets of community features for building member attachment by strengthening either group identity or interpersonal bonds. To increase identity-based attachment, we gave members information about group activities and intergroup competition, and tools for group-level communication. To increase bond-based attachment, we gave members information about the activities of individual members and interpersonal similarity, and tools for interpersonal communication. Results from a six-month field experiment show that participants’ visit frequency and self-reported attachment increased in both conditions. Community features intended to foster identity-based attachment had stronger effects than features intended to foster bond-based attachment. Participants in the identity condition with access to group profiles and repeated exposure to their group’s activities visited their community twice as frequently as participants in other conditions. The new features also had stronger effects on newcomers than on old-timers. This research illustrates how theory from the social science literature can be applied to gain a more systematic understanding of online communities and how theory-inspired features can improve their success