Using attractiveness model for actors ranking in social media networks

Ziyaad Qasem,Marc Jansen,H Ulrich Hoppe,Tobias Hecking

doi:10.1186/s40649-017-0040-8

Ziyaad Qasem, Marc Jansen + Show 2 more

Open Access

https://doi.org/10.1186/s40649-017-0040-8

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Abstract

BackgroundInfluential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc.Proposed methodsThis work aims to extend our formally defined T measure to present a new measure aiming to recognize the actor’s influence by the strength of attracting new important actors into a networked community. Therefore, we propose a model of the actor’s influence based on the attractiveness of the actor in relation to the number of other attractors with whom he/she has established connections over time.Results and conclusionsUsing an empirically collected social network for the underlying graph, we have applied the above-mentioned measure of influence in order to determine optimal seeds in a simulation of influence maximization. We study our extended measure in the context of information diffusion because this measure is based on a model of actors who attract others to be active members in a community. This corresponds to the idea of the IC simulation model which is used to identify the most important spreaders in a set of actors.

Highlights

Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc
We simulated the information diffusion based on the independent cascade (IC) model with time-respecting paths for seed sets of sizes n = 1 . . . 25 which are generated from different influence measures
In summary, we presented in this paper an extended approach to detect influential actors based on the attractiveness model that is introduced with T measure

Summary

Introduction

Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc. Proposed methods: This work aims to extend our formally defined T measure to present a new measure aiming to recognize the actor’s influence by the strength of attracting new important actors into a networked community. We study our extended measure in the context of information diffusion because this measure is based on a model of actors who attract others to be active members in a community. This corresponds to the idea of the IC simulation model which is used to identify the most important spreaders in a set of actors. A viral marketing operation for a new product can be conducted by seeding the product in Twitter with a few elected influential actors who can influence others in a way that might help in the rapid spread of that product

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