Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 2, 20 February 2023
Open
Access | Article
Similarities of Influencers across Different Social Media Platforms by Using Four Centrality Measures
* Author to whom correspondence should be addressed.
Abstract
Searching for influencers among a social network is important because marketers can then use this information to conduct word-of-mouth (WOM) advertisement, which is an important marketing technique. Literature Review provides detailed information about WOM advertisement. There are many ways to search influencers and often they are network centrality measurements. This paper aims to investigate whether each centrality measurement could produce similar results across different social media platforms (eg. Facebook, Twitter, Instagram). The social network data used in this research is from Huawei Company. This research uses four centrality measurements and three set similarity methods to analysis the data. As a result, this paper draws a conclusion about the binary question "Does it provide similar results or not?". Since various companies and applications may have different standards and definitions about being similar, please also check similarity data provided in this paper.
Keywords
marketing strategy, centrality measurements, social network
References
1. Auxuer B and Anderson M 2021 Social media use in 2021 Pew Research Center https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/
2. Akbari M, Foroudi P, Fashami RZ, Nasrin M and Khodayari 2022 Let us talk about something: The evolution of e-WOM from the past to the future J.Bus.Res 149 663-89
3. Kumar V, Petersen J, and P.Leone R 2007 How valuable is word of mouth? Har.Bus.Review 85(10) 139
4. Keller E and Berry J 2003 The Influentials:One American in ten tells the other nine how to vote, where to eat, what to buy (New York: The Free Press) p 279
5. Kiss C and Bichler M 2008 Identification of influencers — Measuring influence in customer networks Decision.Support.System 46 233-53
6. Rodrigues FA 2019 Network Centrality: An Introduction A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems Macau E vol 22 (Springer, Cham) p 177-196
7. Zhang J and Yu L 2017 Degree centrality, betweenness centrality, and close-ness centrality in social network MSAM2017 132 300-3
8. Ruhnau B 2000 Eigenvector-centrality—a node-centrality Social.Networks 22 357-65
9. Koschuetzki D and Schreiber F 2004 Comparison of centralities for biological networks GermanConf.Bioinformatics(GCB'04) 199-206
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)
- ISBN (Print)
- 978-1-915371-13-3
- ISBN (Online)
- 978-1-915371-14-0
- Published Date
- 20 February 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/2/20220133
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited