Towards understanding a football club's social media network: an exploratory case study of Manchester United. Issue 1 (23rd January 2021)

• Record Type: Journal Article
• Authors: Guzmán, Erick Méndez; Zhang, Ziqi; Ahmed, Wasim
• Is Part Of: Information discovery and delivery. Volume 49:Issue 1(2021)
• Page Start: 71
• Page End: 83
• Publication Date: 2021-01-23
• Subjects: Information retrieval -- World Wide Web -- Social media -- Network analysis -- Information networks -- Online computing -- Manchester United -- Football -- Content analysis; Information retrieval -- Periodicals; Document delivery -- Periodicals; Digital libraries -- Periodicals; Information storage and retrieval systems -- Periodicals; 025.524
• DOI: 10.1108/IDD-08-2020-0106
• Languages: English
• ISSNs: 2398-6247

Abstract

Abstract : Purpose: The purpose of this work is to study how different stakeholders of a football club engage with interactions online through Twitter. It analyses the football club's Twitter network to discover influential actors and the topic of interest in their online communication. Design/methodology/approach: The authors analysed the social networks derived from over two million tweets collected during football matches played by Manchester United. The authors applied social network analysis to discover influencers and sub-communities and performed content analysis on the most popular tweets of the prominent influencers. Findings: Sub-communities can be formed around current affairs that are irrelevant to football, perhaps due to opportunistic attempts of using the large networks and massive attention during football matches to disseminate information. Furthermore, the popularity of tweets featuring different topics depends on the types of influencers involved. Practical implications: The methods can help football clubs develop a deeper understanding of their online social communities. The findings can also inform football clubs on how to optimise their communication strategies by using various influencers. Originality/value: Compared to previous research, the authors discovered a wide range of influencers and denser networks characterised by a smaller number of large clusters. Interestingly, this study also found that bots appeared to become influential within the network.