- users are continuously flooded with news, and may , therefore, become increasingly inert to further stimuli
- there are specific hight influence communities that engage a substantial part of the system. The paper shows how to identify these communities.
- impact defines how likely a user will respond to a certain stimuli. These responses are measured in terms of replies.
- spread of diseases in a population
- spread of influence across a social network
- cross-posts in USENET
- Wu et al. use a reply-to network as a proxy for information flow. Such reply-to relations have similar properties than friendship relations within social networks.
The Independent Cascade Model (ICM) for Diffusion
- introduced by Kempe et al. (2003)
- similar to spreading activation:
- Graph: G=(V,E)
- Vertices (V) refer to actors and have a certain (probabilistic) activation function
- Edges (E) refer to the probability of an information flow between an actor i and an actor j.
- the diffusion process starts with seed nodes that try to activate their neighbors at every iteration
- the method has been applied to the boards.ie forum based on 51 temporal snapshots that have been taken within a year
- an experiment has let to a wait time $$\tau$$ = 1 week between original post and reply (84% of all responding users responded to a post within one week)
- the edge's weights reflect the likelihood of a flow between user i and j (reply from j to i divided by the total number of replies from j).
- all measures have been computed based on sets of sample users per community
- impact measure considers
- a user's degree of membership, and
- its centrality within a community
- impact (centrality * membership)
- normalized impact that normalizes this measure with the community size
- total impact (subtracts a communities self-impact values)
- entropy (how many distinct communities are influenced by the seed community)