Communities

会議の名前
CHI 2022
Coordination and Collaboration: How do Volunteer Moderators Work as a Team in Live Streaming Communities?
要旨

Volunteer moderators (mods) play significant roles in developing moderation standards and dealing with harmful content in their micro-communities. However, little work explores how volunteer mods work as a team. In line with prior work about understanding volunteer moderation, we interview 40 volunteer mods on Twitch --- a leading live streaming platform. We identify how mods collaborate on tasks (off-streaming coordination and preparation, in-stream real-time collaboration, and relationship building both off-stream and in-stream to reinforce collaboration) and how mods contribute to moderation standards (collaboratively working on the community rulebook and individually shaping community norms). We uncover how volunteer mods work as an effective team. We also discuss how the affordances of multi-modal communication and informality of volunteer moderation contribute to task collaboration, standards development, and mod's roles and responsibilities.

著者
Jie Cai
New Jersey Institute of Technology, Newark, New Jersey, United States
Donghee Yvette Wohn
New Jersey Institute of Technology, Newark , New Jersey, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517628

動画
Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI
要旨

As an emerging interaction paradigm, physiological computing is increasingly being used to both measure and feed back information about our internal psychophysiological states. While most applications of physiological computing are designed for individual use, recent research has explored how biofeedback can be socially shared between multiple users to augment human-human communication. Reflecting on the empirical progress in this area of study, this paper presents a systematic review of 64 studies to characterize the interaction contexts and effects of social biofeedback systems. Our findings highlight the importance of physio-temporal and social contextual factors surrounding physiological data sharing as well as how it can promote social-emotional competences on three different levels: intrapersonal, interpersonal, and task-focused. We also present the Social Biofeedback Interactions framework to articulate the current physiological-social interaction space. We use this to frame our discussion of the implications and ethical considerations for future research and design of social biofeedback interfaces.

著者
Clara Moge
University College London, London, United Kingdom
Katherine Wang
University College London, London, United Kingdom
Youngjun Cho
UCL, London, United Kingdom
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517495

動画
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
要旨

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement---and future engagement when mediated by authors---without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.

著者
Hyeonsu B. Kang
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Rafal Dariusz. Kocielnik
University of Washington, Seattle, Washington, United States
Andrew Head
Allen Institute for AI, Seattle, Washington, United States
Jiangjiang Yang
Allen Institute of AI, Seattle, Washington, United States
Matt Latzke
Allen Institute for AI, Seattle, Washington, United States
Aniket Kittur
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Daniel S. Weld
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
Doug Downey
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
Jonathan Bragg
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517470

動画
Follow the Leader: Technical and Inspirational Leadership in Open Source Software
要旨

We conduct the first comprehensive study of the behavioral factors which predict leader emergence within open source software (OSS) virtual teams. We leverage the full history of developers' interactions with their teammates and projects at \github.com between January 2010 and April 2017 (representing about 133 million interactions) to establish that -- contrary to a common narrative describing open source as a pure "technical meritocracy" -- developers' communication abilities and community building skills are significant predictors of whether they emerge as team leaders. Inspirational communication therefore appears as central to the process of leader emergence in virtual teams, even in a setting like OSS, where technical contributions have often been conceptualized as the sole pathway to gaining community recognition. Those results should be of interest to researchers and practitioners theorizing about OSS in particular and, more generally, leadership in geographically dispersed virtual teams, as well as to online community managers.

著者
Jérôme Hergueux
French National Center for Scientific Research (CNRS, BETA lab), Strasbourg, France
Samuel Kessler
University of Oxford, Oxford, United Kingdom
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517516

動画