A Collaborative Virtual Environment or CVE is a distributed, virtual reaEity that is designed to support collaborative activities, As such, CVEs provide a potentiaIly infinite, graphically malised digital landscape within which multiple users can interact with each other and with simple or complex data representations. CVEs are increasingly being used to support collaborative work between geographically separated and between collocated collaborators. CVEs vary in the sophistication of the data and embodiment representations employed and in the level of interactivity supported. It is clear that systems which are intended to support collaborative activities should be designed with explicit consideration of the tasks to be achieved and the intended users' social and cognitive characteristics. In this paper, we detail a number Qf existing systems and appfications, but first discuss the nature of collaborative and cooperative work activities and consider the place of virtual reality systems in supporting such coTlaborative work. Following this, we discuss some future research directions.

As online communities grow and the volume of usergenerated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors, and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task? In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions. The system shows great potential for semiautomated community management.

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Abstract. Community poster boards serve an important community building function. Posted fliers advertise services, events and people's interests, and invite community members to communicate, participate, interact and transact. In this paper we describe the design, development and deployment of several large screen, digital community poster boards, the Plasma Posters, within our organization. We present our motivation, two fieldwork studies of online and offline information sharing, and design guidelines derived from our observations. After introducing the Plasma Posters and the underlying information storage and distribution infrastructure, we illustrate their use and value within our organization, summarizing findings from qualitative and quantitative evaluations. We conclude by elaborating socio-technical challenges we have faced in our design and deployment process.

Past, present and emerging technologies of memory are important concerns for memory studies. What is remembered individually and collectively depends in part on technologies of memory and socio-technical practices, which are changing radically. We identify specific concerns about developments in digital memory capture, storage and retrieval. Decisions are being made now that may have far-reaching consequences. Systems are being designed based on models and metaphors in which human memory works much like the computer. We bring to this discussion a critical perspective from science and technology studies (STS) and a grounding in human—computer interaction (HCI) and computer-supported cooperative work (CSCW). We argue that, while these developments are significant for memory studies research, even more important is the need for memory studies to remind and inspire designers of what is possible and useful, and help expand the understanding of human memory on which these systems are based.