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Marketing campaign targeting using bridge extraction in multiplex social network

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Abstract

In this paper, we introduce a methodology for improving the targeting of marketing campaigns using bridge prediction in communities based on the information of multilayer online social networks. The campaign strategy involves the identification of nodes with high brand loyalty and top-ranking nodes in terms of participation in bridges that will be involved in the evolution of the graph. Our approach is based on an efficient classification model combining topological characteristics of crawled social graphs with sentiment and linguistic traits of user-nodes, popularity in social media as well as meta path-based features of multilayer networks. To validate our approach we present a set of experimental results using a well-defined dataset from Twitter and Foursquare. Our methodology is useful to recommendation systems as well as to marketers who are interested to use social influence and run effective marketing campaigns.

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Notes

  1. https://foursquare.com/.

  2. http://www.tweepy.org.

  3. https://pypi.python.org/pypi/pyfoursquare.

  4. https://networkx.github.io.

  5. https://textblob.readthedocs.io.

  6. https://bitbucket.org/taynaud/python-louvain.

  7. http://scikit-learn.org.

  8. https://pypi.python.org/pypi/xgboost.

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Authors and Affiliations

  1. IIT-CNR, Pisa, Italy

    Pantelis Vikatos

  2. Sentiance NV, 2018, Antwerp, Belgium

    Prokopios Gryllos

  3. Computer Engineering and Informatics Department, University of Patras, 26504, Patras, Greece

    Christos Makris

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  1. Pantelis Vikatos
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  2. Prokopios Gryllos
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  3. Christos Makris
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Correspondence to Pantelis Vikatos.

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This article is a revised and extended version of the article: Gryllos, Prokopios, Christos Makris, and Pantelis Vikatos. Marketing campaign targeting using bridge extraction. Proceedings of the Symposium on Applied Computing. ACM, 2017.

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Vikatos, P., Gryllos, P. & Makris, C. Marketing campaign targeting using bridge extraction in multiplex social network. Artif Intell Rev 53, 703–724 (2020). https://doi.org/10.1007/s10462-018-9675-6

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