Marketing Campaigns in Twitter Using a Pattern Based Diffusion Policy

In this paper we introduce a novel methodology to achieve information diffusion within a social graph that activates a realistic number of users. Our approach combines the predicted patterns of diffusion for each node with propagation heuristics in order to achieve an effective cover of the graph. T...

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Bibliographic Details
Published in2016 IEEE International Congress on Big Data (BigData Congress) pp. 125 - 132
Main Authors Kafeza, Eleanna, Makris, Christos, Vikatos, Pantelis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Computational modeling
Diffusion processes
Information diffusion
Integrated circuit modeling
Probabilistic logic
Social influence
Social media analytics
Twitter
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Summary:In this paper we introduce a novel methodology to achieve information diffusion within a social graph that activates a realistic number of users. Our approach combines the predicted patterns of diffusion for each node with propagation heuristics in order to achieve an effective cover of the graph. The novelty of our methodology is based on the use of history information to predict users' diffusion patterns and on our proposed PBD heuristics for achieving a realistic information spread. Moreover, we use a methodology for calculating the actual diffusion of a message in a social media graph. To validate our approach we present a set of experimental results. Our methodology is useful to marketers who are interested to use social influence and run effective marketing campaigns.
DOI:10.1109/BigDataCongress.2016.24