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Marketing campaign targeting using bridge extraction

Published: 03 April 2017 Publication History

Abstract

In this paper we introduce a methodology for improving targeting of marketing campaigns using bridge prediction in influential communities. The campaign strategy involves the identification of nodes with high brand loyalty and top-ranking nodes in terms on frequency of bridges that will be involved in the evolution of the graph. Our approach is based on an efficient classification model combining topological characteristics of a crawled social graph with sentiment and linguistic traits of user-nodes and influence in social media. To validate our approach we present a set of experimental results using a well-defined dataset from Twitter. 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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  • (2023)The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09374-x34:2(323-374)Online publication date: 12-Aug-2023
  • (2020)Data Architecture: A Sustainable Foundation for Data ExploitationIEEE Potentials10.1109/MPOT.2020.301458939:6(15-21)Online publication date: Nov-2020

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  1. Marketing campaign targeting using bridge extraction

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    cover image ACM Conferences
    SAC '17: Proceedings of the Symposium on Applied Computing
    April 2017
    2004 pages
    ISBN:9781450344869
    DOI:10.1145/3019612
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    Published: 03 April 2017

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    Author Tags

    1. graph mining
    2. link prediction
    3. sentiment analysis
    4. social marketing

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    SAC 2017: Symposium on Applied Computing
    April 3 - 7, 2017
    Marrakech, Morocco

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    • (2023)The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09374-x34:2(323-374)Online publication date: 12-Aug-2023
    • (2020)Data Architecture: A Sustainable Foundation for Data ExploitationIEEE Potentials10.1109/MPOT.2020.301458939:6(15-21)Online publication date: Nov-2020

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