This document discusses using artificial agents to manage social attention in online communities. It proposes a social translucence design approach where agents observe community activity, detect behavioral patterns, and send recommendations to users. Basic agents could notify new users or those with incomplete profiles, while advanced agents might detect bursts of collective activity on a resource and notify the owner. The goal is to emphasize the social aspects of the community and increase participation through raising awareness of behaviors and collective activity.

1. Social Attention Management in Online Community using Artificial Agents Nicolas Maisonneuve, Research Associate Centre for Advanced Learning Technologies, INSEAD Context: European research project www.atgentive.com 2008

3. Emergence of social mechanisms (trust , affinity)

5. Process 1/ CommunityActivity (reading, tagging, voting, exchangingknowledge) Activity visible via RSS 2/ ExternalArtificial Agents observe the activity A1 A2 A3 4/ Raising of awareness Usersare aware of badbehavior/hidden collective activity 3/ When an agent detects a given activity pattern, it recommends an action by sending a message to the given user(s)

8. Social translucence design related to resources Diffusion in the community / audience of a resource - is my message well diffused in the community? - Is everybody aware about these posting? Lifecycle of a resource - “is the resource dead?” - “is there a burst of activity now or these last days?” Social aspect - who were the last readers? - who was interested by this document? Approach  Resource/Tag as a memory of the social activity  Showing the social activity related a resource or a Tag with the resources to emphasize its social aspect using web widget Web widget