A Tale of Two Virtual Communities: A comparative analysis of culture and discourse in two online programming communities

Software programming is increasingly becoming a collaborative and community driven effort, with online discussions becoming vital resources for learning and knowledge sharing. This study explores the differences in the discourse patterns of two popular online programming communities and provides insights into the type of community practices and learning outcomes these collectives support and scaffold. A three step content analysis framework is presented that employs a mixture of automated text processing techniques and qualitative methods on a representative sample of 8639 and 6126 contributions from Stack Overflow and r/Askprogramming respectively. Results indicate differences between communities in the scope of topics and the nature of responses provided. While r/Askprogramming has a more community centric, interpersonal approach and provides a space for sharing and supporting needs beyond knowledge sharing and factual learning, Stack Overflow takes a more task focused, knowledge centric approach. These findings suggest key normative structures that regulate patterns of collaboration and deliberation, which may have long term design implications for structuring and sustaining informal learning initiatives that nurture and promote technical skill development and enhancement

“Envisioning Digital Sanctuaries”: An Exploration of Virtual Collectives for Nurturing Professional Development of Women in Technical Domains

Work and learning are essential facets of our existence, yet sociocultural barriers have historically limited access and opportunity for women in multiple contexts, including their professional pursuits. Such sociocultural barriers are particularly pronounced in technical domains and have relegated minoritized voices to the margins. As a result of these barriers, those affected have suffered strife, turmoil, and subjugation. Hence, it is important to investigate how women can subvert such structural limitations and find channels through which they can seek support and guidance to navigate their careers. With the proliferation of modern communication infrastructure, virtual forums of conversation such as Reddit have emerged as key spaces that allow knowledge-sharing, provide opportunities for mobilizing collective action, and constitute sanctuaries of support and companionship. Yet, recent scholarship points to the negative ramifications of such channels in perpetuating social prejudice, directed particularly at members from historically underrepresented communities. Using a novel comparative muti-method, multi-level empirical approach comprising content analysis, social network analysis, and psycholinguistic analysis, I explore the way in which virtual forums engender community and foster avenues for everyday resilience and collective care through the analysis of 400,267 conversational traces collected from three subreddits (r/cscareerquestions, r/girlsgonewired & r/careerwoman). Blending the empirical analysis with a novel theoretical apparatus that integrates insights from social constructivist frameworks, feminist data studies, computer-supported collaborative work, and computer-mediated communication, I highlight how gender, care, and community building intertwine and collectively impact the emergent conversational habits of these online enclaves. Key results indicate six content themes ranging from discussions on knowledge advancement to scintillating ethical probes regarding disparities manifesting in the technical workplace. Further, psycholinguistic and network insights reveal four pivotal roles that support and enrich the communities in different ways. Taken together, these insights help to postulate an emergent spectrum of relationality ranging from a more agentic to a more communal pattern of affinity building. Network insights also yield valuable inferences regarding the role of automated agents in community dynamics across the forums. A discussion is presented regarding the emergent routines of care, collective empowerment, empathy-building tactics, community sustenance initiatives, and ethical perspectives in relation to the involvement of automated agents. This dissertation contributes to the theory and practice of how virtual collectives can be designed and sustained to offer spaces for enrichment, empowerment, and advocacy, focusing on the professional development of historically underrepresented voices such as women

"Digital Sanctums of Empowerment": Exploring Community and Everyday Resilience Building Tactics in Online Professional Communities for Women

Work and learning are essential facets of our existence, yet women have and may continue to face multiple restrictions that hinder and impede their professional outcomes. These restrictions are especially pronounced in the technical domains of Information technology and Computer science. This paper explores the power of informal online communities to act as collective shields of care in the context of professional development, especially for women. Using a mixed-methods comparative investigation of 400,268 conversational traces from three professional development communities on Reddit, we report resilience and communal empathy-building tactics, as well as calls for inclusivity and belongingness, which drive the collective identity of these online channels. The long-term goal of this work is to address the way in which such channels can be designed and curated to offer spaces for enrichment, empowerment, and advocacy with a focus on the professional development of women, especially those engaged in technical domains

Learning with comments: An analysis of comments and community on Stack Overflow

Stack Overflow (SO) has become a primary source for learning how to code, with community features supporting asking and answering questions, upvoting to signify approval of content, and comments to extend questions and answers. While past research has considered the value of posts, often based on upvoting, little has examined the role of comments. Beyond value in explaining code, comments may offer new ways of looking at problems, clarifications of questions or answers, and socially supportive community interactions. To understand the role of comments, a content analysis was conducted to evaluate the key purposes of comments. A coding schema of nine comment categories was developed from open coding on a set of 40 posts and used to classify comments in a larger dataset of 2323 comments from 50 threads over a 6-month period. Results provide insight into the way the comments support learning, knowledge development, and the SO community, and the use and usefulness of the comment feature

IUIoT: Intelligent user interfaces for IoT

As IoT devices begin to permeate our environment, our interaction with these devices are starting to have a real potential to transform our daily lives. Therefore, there exists an incredible opportunity for intelligent user interfaces to simplify the task of controlling such devices. The goal of IUIoT workshop was to serve as a platform for researchers who are working towards the design of IoT systems from an intelligent, human-centered perspective. The workshop accepted a total of five papers: two position and three extended abstracts. These papers were presented by the authors and discussed among the workshop attendees with an aim of exploring future directions and improving existing approaches towards designing intelligent User Interfaces for IoT environments

To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other’s working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality-derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.Web Information System