Cognitive Analysis in Social Networks for Viral Marketing

Viral marketing is the modern version of the old "word-of-mouth" advertising, where companies choose a restricted number of persons, considered "influential," recommending them products or services that will be in turn iteratively suggested. In this article, we propose cognitive...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2021-09, Vol.17 (9), p.6162-6169
Main Authors: Castiglione, Aniello, Cozzolino, Giovanni, Moscato, Francesco, Moscato, Vincenzo
Format: Article
Language:English
Subjects:
Advertising
Algorithms
Analytical models
Cognitive analytics
Combinatorial analysis
influence diffusion
influence maximization (IM)
Informatics
Marketing
Mood
online social networks (OSN) models
Social network services
Social networks
Viral marketing
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Summary:Viral marketing is the modern version of the old "word-of-mouth" advertising, where companies choose a restricted number of persons, considered "influential," recommending them products or services that will be in turn iteratively suggested. In this article, we propose cognitive models and algorithms for marketing applications through online social networks, considered as a graph database, and define the concept of influence graph leveraging particular user behavioral patterns, by querying the initial heterogeneous graph network. We also model the diffusion across the network, without any preliminary information, as a combinatorial multiarmed bandit problem, for the selection of most influential users. We have used the YELP social network as a case study for our approach, showing how it is possible to generate an influence graph considering several kinds of relevant paths (mainly considering reviews to the same firms) by which a user can influence other ones. Several experiments have been carried out and discussed, putting into evidence the effectiveness and efficacy of the proposed methods for influence maximization with respect to other approaches of state of the art.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3026013