# Targeting Online Communities to Maximise Information Diffusion

BelÃ¡k, V., Lam, S. & Hayes, C., 2012. Targeting online communities to maximise information diffusion. In Proceedings of the 21st international conference companion on World Wide Web. WWW ™12 Companion. New York, NY, USA: ACM, pp. 1153—1160.

## Introduction

The authors investigate how to maximize information flows across communities and introduce a novel measure for cross-community influence.

1. users are continuously flooded with news, and may , therefore, become increasingly inert to further stimuli
2. there are specific hight influence communities that engage a substantial part of the system. The paper shows how to identify these communities.
3. impact defines how likely a user will respond to a certain stimuli. These responses are measured in terms of replies.

## Related Work

1. spread of diseases in a population
2. spread of influence across a social network
3. cross-posts in USENET
4. 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

1. introduced by Kempe et al. (2003)
• 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

## Method

### Experimental setup

1. the method has been applied to the boards.ie forum based on 51 temporal snapshots that have been taken within a year
2. 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)
3. 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).
4. all measures have been computed based on sets of sample users per community

### Influence Measures

1. impact measure considers
• a user's degree of membership, and
• its centrality within a community

2. the authors introduce
• 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)

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