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Influential spreaders in the political Twitter sphere of the 2013 Malaysian general election

Hong-liang Sun (School of Information and Engineering, Nanjing University of Finance and Economics, Nanjing, China) (International Doctoral Innovation Centre, University of Nottingham, Ningbo, China)
Eugene Ch’ng (NVIDIA Technology Centre, University of Nottingham, Ningbo, China)
Simon See (NVIDIA Technology Centre, Singapore)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 3 October 2018

Issue publication date: 8 February 2019

652

Abstract

Purpose

The purpose of this paper is to investigate political influential spreaders in Twitter at the juncture before and after the Malaysian General Election in 2013 (MGE2013) for the purpose of understanding if the political sphere within Twitter reflects the intentions, popularity and influence of political figures in the year in which Malaysia has its first “social media election.”

Design/methodology/approach

A Big Data approach was used for acquiring a series of longitudinal data sets during the election period. The work differs from existing methods focusing on the general statistics of the number of followers, supporters, sentiment analysis, etc. A retweeting network has been extracted from tweets and retweets and has been mapped to a novel information flow and propagation network we developed. The authors conducted quantitative studies using k-shell decomposition, which enables the construction of a quantitative Twitter political propagation sphere where members posited at the core areas are more influential than those in the outer circles and periphery.

Findings

The authors conducted a comparative study of the influential members of Twitter political propagation sphere on the election day and the day after. The authors found that representatives of political parties which are located at the center of the propagation network are winners of the presidential election. This may indicate that influential power within Twitter is positively related to the final election results, at least in MGE2013. Furthermore, a number of non-politicians located at the center of the propagation network also significantly influenced the election.

Research limitations/implications

This research is based on a large electoral campaign in a specific election period, and within a predefined nation. While the result is significant and meaningful, more case studies are needed for generalized application for identifying potential winning candidates in future social-media fueled political elections.

Practical implications

The authors presented a simple yet effective model for identifying influential spreaders in the Twitter political sphere. The application of the authors’ approach yielded the conclusion that online “coreness” score has significant influence to the final offline electoral results. This presents great opportunities for applying the novel methodology in the upcoming Malaysian General Election in 2018. The discovery presented here can be used for understanding how different players of political parties engage themselves in the election game in Twitter. The approach can also be adopted as a factor of influence for offline electoral activities. The conception of a quantitative approach in electoral results greatly influenced by social media means that comparative studies could be made in future elections.

Originality/value

Existing works related to general elections of various nations have either bypassed or ignored the subtle links between online and offline influential propagations. The modeling of influence from social media using a longitudinal and multilayered approach is also rarely studied. This simple yet effective method provides a new perspective of practice for understanding how different players behave and mutually shape each other over time in the election game.

Keywords

Citation

Sun, H.-l., Ch’ng, E. and See, S. (2019), "Influential spreaders in the political Twitter sphere of the 2013 Malaysian general election", Industrial Management & Data Systems, Vol. 119 No. 1, pp. 54-68. https://doi.org/10.1108/IMDS-09-2017-0409

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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