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Authors: Chiara Francalanci and Ajaz Hussain

Affiliation: Politecnico di Milano, Italy

ISBN: 978-989-758-035-2

Keyword(s): Sentiment Analysis, Semantic Networks, Power Law Graphs, Social Influence.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Analytics ; Data Engineering ; Data Management and Quality ; Data Modeling and Visualization ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Discovery and Information Retrieval ; Knowledge Influence and Influencers ; Knowledge Management ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Semantics and Social Media ; Society, e-Business and e-Government ; Statistics Exploratory Data Analysis ; Symbolic Systems ; Web Information Systems and Technologies

Abstract: This paper starts from the observation that social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. In social media, hub nodes represent social influencers. However, the literature provides evidence of the multi-layered structure of influence networks, emphasizing the distinction between influencers and influence. The latter seems to spread following multi-hop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation and exploration of peripheral layers and clusters to exploit underlying concept of k-shell decomposition analysis. The core concept of our approach is to partition the node set of a graph into hub and peripheral nodes. Then, a power-law based modified force-directed method is applied to clearly display local multi-layered neighbourhood clusters around hub nodes. Our approach is tested on a large sample of tweets from the tourism domain. Empirical results indicate that peripheral nodes have a greater probability of being retweeted and, thus, play a critical role in determining the influence of content. Our visualization technique helps us highlight peripheral nodes and, thus, seems an interesting tool to the visual analysis of social influence. (More)

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Paper citation in several formats:
Francalanci, C. and Hussain, A. (2014). A Visual Approach to the Empirical Analysis of Social Influence.In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 319-330. DOI: 10.5220/0004992803190330

@conference{data14,
author={Chiara Francalanci. and Ajaz Hussain.},
title={A Visual Approach to the Empirical Analysis of Social Influence},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={319-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004992803190330},
isbn={978-989-758-035-2},
}

TY - CONF

JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - A Visual Approach to the Empirical Analysis of Social Influence
SN - 978-989-758-035-2
AU - Francalanci, C.
AU - Hussain, A.
PY - 2014
SP - 319
EP - 330
DO - 10.5220/0004992803190330

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