Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Analyzing network dynamics and dominant hate speech types in Twitter conversations during professional football matches

View through CrossRef
Introduction: Persib is one of Indonesia's most popular football clubs, and it has a strong fan base. Unfortunately, this does not prevent it from being exposed to negative opinions when it competes. Objective: This study aims to describe the structural features of the networks and identify the dominant types of hate speech while Persib is in a match on Twitter. Methodology: This research employed a two-phase, mixed-method approach of a digital netnography with social network analysis and thematic content analysis of 413,688 tweets during the Liga 1 Sport Event. Results: This study's findings indicate that Persib is very vulnerable to receive hate speech from fans when they are in match. Based on the analysis, the most frequently occurring speech was insulting or cursing, blaming, threatening, satirical, and critical. Discussion: This hate speech is directed at Persib players, coaches, and management. Hate speech directed at Persib was dominated by local languages ​​or Sundanese. The topics of hate speech were primarily related to the course of the match, player performance, ticket system, management, and the broadcast of the match, which was considered lacking. Conclusions: These findings can be used to evaluate Persib management and provide a basis for developing strategies to combat hate speech on Twitter. Hate speech experienced by Persib also occurred because Persib fans believe that Persib as a football club only focuses on social media image building. Thus, Persib must consider the hate speech that befell Persib as urgent and need to be handled immediately because it can potentially threaten Persib's image.
Title: Analyzing network dynamics and dominant hate speech types in Twitter conversations during professional football matches
Description:
Introduction: Persib is one of Indonesia's most popular football clubs, and it has a strong fan base.
Unfortunately, this does not prevent it from being exposed to negative opinions when it competes.
Objective: This study aims to describe the structural features of the networks and identify the dominant types of hate speech while Persib is in a match on Twitter.
Methodology: This research employed a two-phase, mixed-method approach of a digital netnography with social network analysis and thematic content analysis of 413,688 tweets during the Liga 1 Sport Event.
Results: This study's findings indicate that Persib is very vulnerable to receive hate speech from fans when they are in match.
Based on the analysis, the most frequently occurring speech was insulting or cursing, blaming, threatening, satirical, and critical.
Discussion: This hate speech is directed at Persib players, coaches, and management.
Hate speech directed at Persib was dominated by local languages ​​or Sundanese.
The topics of hate speech were primarily related to the course of the match, player performance, ticket system, management, and the broadcast of the match, which was considered lacking.
Conclusions: These findings can be used to evaluate Persib management and provide a basis for developing strategies to combat hate speech on Twitter.
Hate speech experienced by Persib also occurred because Persib fans believe that Persib as a football club only focuses on social media image building.
Thus, Persib must consider the hate speech that befell Persib as urgent and need to be handled immediately because it can potentially threaten Persib's image.

Related Results

Faith Tweets: Ambient Religious Communication and Microblogging Rituals
Faith Tweets: Ambient Religious Communication and Microblogging Rituals
There’s no reason to think that Jesus wouldn’t have Facebooked or twittered if he came into the world now. Can you imagine his killer status updates? Reverend Schenck, New York, Al...
Alts and Automediality: Compartmentalising the Self through Multiple Social Media Profiles
Alts and Automediality: Compartmentalising the Self through Multiple Social Media Profiles
IntroductionAlt, or alternative, accounts are secondary profiles people use in addition to a main account on a social media platform. They are a kind of automediation, a way of rep...
Vihapuheen kohteet ja teemat sekä lajit ja muodot ennen ja nyt
Vihapuheen kohteet ja teemat sekä lajit ja muodot ennen ja nyt
Tässä artikkelissa on analysoitu vihapuheen olemusta ja puhunnan muotoja 1930- ja 2000-luvuilla. Tavoitteena on ollut etsiä niitä yhtäläisyyksiä ja eroja, joita kahdella eri aikaka...
CURRENT DEVELOPMENT TRENDS OF AMATEUR FOOTBALL IN UKRAINE
CURRENT DEVELOPMENT TRENDS OF AMATEUR FOOTBALL IN UKRAINE
Introduction. Scientists studying the development of football emphasize that professional football is a kind of core or top of the conditional structure of the modern football indu...
Hate Speech Detection Using Textual and User Features
Hate Speech Detection Using Textual and User Features
Social media platforms provide users with a powerful platform to share their ideas. Using one’s right to expression to incite hatred toward a particular group of people ...
Forensic Linguistics of Hate Speech on Social Media against President Joko Widodo by Chairman of UGM’s Student Executive Board
Forensic Linguistics of Hate Speech on Social Media against President Joko Widodo by Chairman of UGM’s Student Executive Board
This research discusses the hate speech delivered by the chairman of BEM UGM against President Joko Widodo, uploaded on social media. This research uses a forensic linguistic appro...
A Twitter Sentimen Analysis on Islamic Banking Using Drone Emprit Academic (DEA): Evidence from Indonesia
A Twitter Sentimen Analysis on Islamic Banking Using Drone Emprit Academic (DEA): Evidence from Indonesia
ABSTRACT The research aimed to identify and collect issues discussed regarding Islamic banking from user activity, sentimen, and content on Twitter. This study used a qualitative a...

Back to Top