Social attention management in online communities

The objective of this research was to investigate the use of Information and Communication Technologies to support people in managing their attention at a social level. People are being overwhelmed by solicitations and opportunities to engage into a social exchange but they have little means about how to deal effectively with this new level of interaction. I present here few slides corresponding to my research on this topic in the context of an european research project Atgentive ( at the Institut européen d’administration des affaires (INSEAD).

An attention-based Ranking Model for social media

Internet increases people’s social capital through increasing connections among friends/professional people. Associated to this change, a new behavior described by L.Stone as continual partial attention comes up: people attempt to stay partially but continuously aware about the activity within their networks. A typical situation for a user in a rich social environment is to decide from a list of incoming messages which ones are the most important for his limited attention capacity (e.g. limited time to read), keeping him aware of unexpected events and avoiding attentional demands according to his or her current interests. In this paper we propose an attention aware system inspired by the visual attention model of J.M Wolfe[20] where visual stimuli/signals are changed in social stimuli (solicitations of other persons) for our concern.


Maisonneuve, N. (2007). Application of a simple visual attention model to the communication overload problem. In Workshop at UBICOMP 2007 , 9th International Conference on Ubiquitous Computing (Vol. 33)

More details & information (especially about the “Perception Module”)

Social attention analysis (not published but interesting…)

I also started to investigate new indicators to observe collective and individual attention and their underlying relationships.

Community orientation & alignment Indicators

The objective this work was to 1/ find new indicators to understand the orientation of the community’s attention in a given interval of time in 3 different spaces:

  • Resources space (users’ profile, messages): Which resources got the most attention? A list of resources that attracted the most the community’s attention (viewed or created) during an interval of time.
  • Semantic space: which tag got the most attention? (=the sum of audiences for each resources related to a given tag for an interval of time).
  • Social space: Which member got the most attention?(the sum of the audiences for each resources related to a member for an interval of time)

2/For each space, we also would like find an indicator to evaluate the alignment of any user’s attention with her/his community’s one i.e. the similarity of the orientation between his/her attention and the community’s one. Then This evaluation has two subgoals: a) to provide to the user a clear picture of his/her orientation according to the global community’s orientation (Has the user also seen the most popular resources?) (meta-cognition). b) to provide to the user a set of resources to read if he/she desires to improve an alignment about a given resource , tag or user.

More details & information

Social attention Profile

In this preliminar research I investigate new indicators to formalize a user’s attention profile according to 2 types of attention:

  • the attention the network has for a member (who pays attention to me)
  • the attention the member have for his or her network (the people to whom I pay attention

More details & information.

AtgentNet: a social platform for supporting Attention Management in online communities

The objective of this chapter it consists in the adaptation to a social context, of a general model for supporting attention that was proposed by Roda and Nabeth (2008) and that relies on supporting attention at four levels:perception, deliberation, operation and meta-cognition. This chapter also presents how the support of social attention has been operationalised with the design of an attention aware social platform
AtGentNet, and tested for supporting interactions of communities of learners and professionals.


  • T. Nabeth, N. Maisonneuve (2009) “Managing Attention in the Social Web: The AtGentNet Approach”,In C. Roda, human attention in digital environments. Cambridge University Press.
  • T. Nabeth, H. Karlsson, A.A. Angehrn, N. Maisonneuve (2008); A Social Network Platform for Vocational Learning in the ITM Worldwide Network; IST Africa 2008, Windhoek, Namibia
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