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Authors: Ermelinda Oro ; Clara Pizzuti and Massimo Ruffolo

Affiliation: National Research Council (CNR), Italy

ISBN: 978-989-758-298-1

Keyword(s): Social Network, Social Media, Twitter, Influential User, Twitter Influencer, Product Perception, Social Network Analysis, Multilayer Networks, Multilinear Algebra, Tensor Decomposition.

Abstract: The massive amount of information posted by twitterers is attracting growing interest because of the several applications fields it can be utilized, such as, for instance, e-commerce. In fact, tweets enable users to express opinions about products and to influence other users. Thus, the identification of social network key influencers with their products perception and preferences is crucial to enable marketers to apply effective techniques of viral marketing and recommendation. In this paper, we propose a methodology, based on multilinear algebra, that combines topological and contextual information to identify the most influential twitterers of specific topics or products along with their perceptions and opinions about them. Experiments on a real use case regarding smartphones show the ability of the proposed methodology to find users that are authoritative in the social network in expressing their views about products and to identify the most relevant products for these users, alon g with the opinions they express. (More)

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Paper citation in several formats:
Oro E., Pizzuti C. and Ruffolo M. (2018). A Methodology for Identifying Influencers and their Products Perception on Twitter.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 577-584. DOI: 10.5220/0006675405770584

@conference{iceis18,
author={Ermelinda Oro and Clara Pizzuti and Massimo Ruffolo},
title={A Methodology for Identifying Influencers and their Products Perception on Twitter},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={577-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006675405770584},
isbn={978-989-758-298-1},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Methodology for Identifying Influencers and their Products Perception on Twitter
SN - 978-989-758-298-1
AU - Oro E.
AU - Pizzuti C.
AU - Ruffolo M.
PY - 2018
SP - 577
EP - 584
DO - 10.5220/0006675405770584

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