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We present 19 systems that have been developed over the past decade to support collaborative writing over the Web. The aim of this article is to present the state of the art on the use of the Web for collaborative writing and thus (1) help designers improve current systems or define future systems, and (2) help users choose the most appropriate system to support their needs. Among available systems, groups can select from tools to write a document (on-or off-line), collect comments about a document, or maintain a Web site. The lack of experimental data concerning Web-based applications forces designers to use other sources of information to guide their design choices, such as a list of functions that an ideal collaborative writing tool should offer. This list has revealed several potential points for improvement.

Collaborative work with office suite documents such as word processing, spreadsheet and presentation files usually demands special tools and methods. For this application, we have developed TellTable, a relatively simple web-based framework built largely from available software and infrastructure. TellTable allows the use of existing office-suite software in a collaborative manner that is controlled but is familiar to users of common single user software. From the literature and our research, we identify twelve challenges to collaborative editing software that we use in an evaluation checklist: time and space, awareness, communication, private and shared work spaces, intellectual property, simultaneity and locking, protection, workflow, security, file format, platform independence, and user benefit. We then use this checklist to characterize TellTable in comparison to some other collaborative office tools. q

This paper investigates the impact of contradictory emotional content on people's ability to identify the emotion expressed on avatar faces as compared to human faces. Participants saw emotional faces (human or avatar) coupled with emotional texts. The face and text could either display the same or different emotions. Participants were asked to identify the emotion on the face and in the text. While they correctly identified the emotion on human faces more often than on avatar faces, this difference was mostly due to the neutral avatar face. People were no better at identifying a facial expression when emotional information coming from two sources was the same than when it was different, regardless of whether the facial expression was displayed on a human face or on an avatar face. Finally, people were more sensitive to context when trying to identify the emotion in the accompanying text.

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