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Authors: Leonardo Mauro Pereira Moraes and Robson Leonardo Ferreira Cordeiro

Affiliation: Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP and Brazil

Keyword(s): Data Mining, Social Networks of Games, Player Modeling, Classification, Feature Extraction, Data Streams.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Industrial Applications of Artificial Intelligence ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Online games have become a popular form of entertainment, reaching millions of players. Among these players are the game influencers, that is, players with high influence in creating new trends by publishing online content (e.g., videos, blogs, forums). Other players follow the influencers to appreciate their game contents. In this sense, game companies invest in influencers to perform marketing for their products. However, how to identify the game influencers among millions of players of an online game? This paper proposes a framework to extract temporal aspects of the players’ actions, and then detect the game influencers by performing a classification analysis. Experiments with the well-known Super Mario Maker game, from Nintendo Inc., Kyoto, Japan, show that our approach is able to detect game influencers of different nations with high accuracy.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Moraes, L. and Cordeiro, R. (2019). Detecting Influencers in Very Large Social Networks of Games. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 93-103. DOI: 10.5220/0007728200930103

@conference{iceis19,
author={Leonardo Mauro Pereira Moraes. and Robson Leonardo Ferreira Cordeiro.},
title={Detecting Influencers in Very Large Social Networks of Games},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={93-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007728200930103},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Detecting Influencers in Very Large Social Networks of Games
SN - 978-989-758-372-8
IS - 2184-4984
AU - Moraes, L.
AU - Cordeiro, R.
PY - 2019
SP - 93
EP - 103
DO - 10.5220/0007728200930103
PB - SciTePress