Understanding sports fans' motivations by text mining #ChongWeiOurHero

Sports organisations must understand sports fans’ motivations behind their social media interactions to successfully harness it as a tool for relationship marketing. Few studies have directly analysed sports fans’ contributions on social media, and of these, most examined supporters’ contributions b...

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Main Author: Goh, Sherwin Si Win
Other Authors: Kee Ying Hwa Adrian
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70292
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-702922020-09-27T20:20:03Z Understanding sports fans' motivations by text mining #ChongWeiOurHero Goh, Sherwin Si Win Kee Ying Hwa Adrian National Institute of Education DRNTU::Social sciences::Journalism::Reporting on sports Sports organisations must understand sports fans’ motivations behind their social media interactions to successfully harness it as a tool for relationship marketing. Few studies have directly analysed sports fans’ contributions on social media, and of these, most examined supporters’ contributions by reading and sorting each comment. Text mining conducts automated analysis of social media comments, making it much less labour and time consuming than traditional content analysis methods. However, its use has hardly been applied to sport. The purpose of this study is to investigate the suitability of text mining in interpreting supporters’ motivations from their social media contributions. Lee Chong Wei’s loss in the 2016 Rio Olympics badminton men’s singles final was the setting for this study. A qualitative approach using text mining and cluster analysis with R Studio was employed to analyse 16972 English tweets under #chongweiourhero, with the results presented as a hierarchical cluster dendrogram. Results indicate that Lee’s fans had the motives of communicating with him, expressing emotion, offering consolation, expressing appreciation and expressing unity when responding to Lee’s loss at the 2016 Olympics on social media. These categories are similar to those found in the study by Kee et al. (2016) which studied the motivations of Lee’s supporters in a similar setting at the 2012 Olympics, but through traditional content analysis methods. The present study validates the suitability of text mining in interpreting supporters’ motives from their social media contributions, and supports it use in aiding sports organizations to harness social media as marketing tool. Bachelor of Science (Sport Science and Management) 2017-04-18T11:01:51Z 2017-04-18T11:01:51Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70292 en 27 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Social sciences::Journalism::Reporting on sports
spellingShingle DRNTU::Social sciences::Journalism::Reporting on sports
Goh, Sherwin Si Win
Understanding sports fans' motivations by text mining #ChongWeiOurHero
description Sports organisations must understand sports fans’ motivations behind their social media interactions to successfully harness it as a tool for relationship marketing. Few studies have directly analysed sports fans’ contributions on social media, and of these, most examined supporters’ contributions by reading and sorting each comment. Text mining conducts automated analysis of social media comments, making it much less labour and time consuming than traditional content analysis methods. However, its use has hardly been applied to sport. The purpose of this study is to investigate the suitability of text mining in interpreting supporters’ motivations from their social media contributions. Lee Chong Wei’s loss in the 2016 Rio Olympics badminton men’s singles final was the setting for this study. A qualitative approach using text mining and cluster analysis with R Studio was employed to analyse 16972 English tweets under #chongweiourhero, with the results presented as a hierarchical cluster dendrogram. Results indicate that Lee’s fans had the motives of communicating with him, expressing emotion, offering consolation, expressing appreciation and expressing unity when responding to Lee’s loss at the 2016 Olympics on social media. These categories are similar to those found in the study by Kee et al. (2016) which studied the motivations of Lee’s supporters in a similar setting at the 2012 Olympics, but through traditional content analysis methods. The present study validates the suitability of text mining in interpreting supporters’ motives from their social media contributions, and supports it use in aiding sports organizations to harness social media as marketing tool.
author2 Kee Ying Hwa Adrian
author_facet Kee Ying Hwa Adrian
Goh, Sherwin Si Win
format Final Year Project
author Goh, Sherwin Si Win
author_sort Goh, Sherwin Si Win
title Understanding sports fans' motivations by text mining #ChongWeiOurHero
title_short Understanding sports fans' motivations by text mining #ChongWeiOurHero
title_full Understanding sports fans' motivations by text mining #ChongWeiOurHero
title_fullStr Understanding sports fans' motivations by text mining #ChongWeiOurHero
title_full_unstemmed Understanding sports fans' motivations by text mining #ChongWeiOurHero
title_sort understanding sports fans' motivations by text mining #chongweiourhero
publishDate 2017
url http://hdl.handle.net/10356/70292
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