The document discusses evaluating the orientation and alignment of a community's attention on online resources. It aims to understand the orientation of the community's attention on resources like messages and user profiles during a time interval [t1, t2], and help individual users regulate their attention by aligning it with the community. It proposes calculating metrics to measure the alignment of a user's attention with the community's attention at different levels - resources, concepts, and other users. The metrics would indicate the degree of alignment and suggest resources/users for a individual to read to improve their alignment with the community.

1. Community’s Orientation and user’s alignment Nicolas Maisonneuve , Research Associate Centre for Advanced Learning Technologies, INSEAD Context: European research project www.atgentive.com Tags: Attention, community, orientation, alignment, audience, web widget, public API The 22 nd of October 2007

2. Orientation Community & user alignment Objective 1: To understand the orientation of the community’s attention during the interval [t1, t2] following 3 levels: Resource: which resources (message, user’s profile) got the most attention? Attention Space ( an attention focus = a resource)

3. Orientation Community & user alignment Objective 1: To understand the orientation of the community’s attention during the interval [t1, t2] following 3 levels: Resource: which resources (message, user’s profile) got the most attention? Concept: Which concepts (tag/keyword) got the most attention? Attention Space (an attention focus = a concept)

4. Orientation Community & user alignment Objective 1: To understand the orientation of the community’s attention during the interval [t1, t2] following 3 levels: Resource: which resources (message, user’s profile) got the most attention? Concept: Which concepts (tag/keyword) got the most attention? User: which member got the most attention? Attention space (attention focus = a user )

5. Objective1: Orientation of the community’s attention during [t1, t2] Output: For each level, a List of elements in the given space (resources, concepts, users) sorted by their weights in the orientation of the community’s attention. “ their weights?” = Attention metrics = audience Resource Space: Social/Concept Space: , with , the resources related to k (e.g. a concept, a user) Orientation Community & user alignment

6. Orientation Community & user alignment Objective 2: for each level (resource, concept, user), to understand and help a user to regulate his/her attention by aligning it with the community’s one: meta cognition: At which degree i s my attention oriented to the same resources ( or same concepts, same users) as the community’s attention? Regulation: “What should I read, or who should I read to improve my alignment?”

7. Objective 2: Evaluation of the user’s alignment Outputs: Indicators for the user’s alignment At the resource level: 4 states of alignment At the tag and people level (set of resources): Alignment metrics , with R a set of resources (all the resources or the resource related to a given concept or user), Orientation Community & user alignment What metrics do we want? If a lot of resources attracted the community but only a subset has attracted the most part of the audience, and if a user only paid attention to it, she/he will be quite aligned (e.g. 90% ). Focus on me Same focus Only me Inattention

8. Community Orientation & user alignment User experience: the widget Implementation: -Public Server API (Atom+ attention extension) - Web Widget (Yahoo UI/Google Ajax Feed Javascript Libs)

9. Next Step… maybe work quickly done (1 week..) Need to more formalized (+ connection with other research fields) TODO Change of entity (e.g. community, a user , an organization) + alignment between different entities (representing different levels) Alignment user/community [done] - [user/user] Alignment user1/user2 [todo] [Objectif] Alignment userattention/userintention [Time] Alignment userPast/ userPresent [Activity/Engagement] Alignment userWritingContext/ userReadingContext Add the notion of engagement in the metrics ( which concept stimulated the most the participation during [t1, t2]? )