CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER

This study aims to categorize the offensive language identified within the hate speeches produced by K-pop fans on Twitter. It is a concern that the use of hate speeches among the fans would intensify cyberbullying. Nonetheless, little researches have been done on the categorization of offensive lan...

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Main Authors: Siti Marina, Kamil, NurAthirah, Zulrushdi
Format: Proceeding
Language:English
English
Published: The Linguistic Society of Korea 2002
Subjects:
Online Access:http://ir.unimas.my/id/eprint/40813/2/cover.pdf
http://ir.unimas.my/id/eprint/40813/3/Classifying%20Offensive.pdf
http://ir.unimas.my/id/eprint/40813/
http://sicol2022.creatorlink.net/
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spelling my.unimas.ir.408132023-10-10T08:09:08Z http://ir.unimas.my/id/eprint/40813/ CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER Siti Marina, Kamil NurAthirah, Zulrushdi P Philology. Linguistics This study aims to categorize the offensive language identified within the hate speeches produced by K-pop fans on Twitter. It is a concern that the use of hate speeches among the fans would intensify cyberbullying. Nonetheless, little researches have been done on the categorization of offensive languages as previous researches have primarily relied on different data sets thus making it hard to do a comparison of different studies. There have been no benchmark data sets in categorizing offensive languages. A Natural Language Processing toolkit called “Orange” is used to extract tweets within a period with some requirements highlighted. The framework adopted from Wiegand et al. (2018) is used to categorize offensive language in this study. Findings have shown that from the 13,300 tweets that have been streamed, only 469 tweets are identified as hate speeches. Among all the 469 tweets, the insult category tops with 162 tweets, followed by the other category with 150 tweets, the profanity category with 135 tweets, and the abuse category with only 22 tweets. Contrary to what others might have presumed, K-pop fans are always associated with a negative nuance. In the desire to change this, the input in the analysis of hate speeches as well as the categorization of offensive languages gathered from this study has shown that even though the insult category tops the result, this study is not meant to generalize all K-pop fans. A lot of K-pop fans are aware of boundaries seeing that only 22 tweets are identified as abusive. This study will hopefully open up a new beginning to other researchers in this specific study. The Linguistic Society of Korea 2002-08-12 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/40813/2/cover.pdf text en http://ir.unimas.my/id/eprint/40813/3/Classifying%20Offensive.pdf Siti Marina, Kamil and NurAthirah, Zulrushdi (2002) CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER. In: Seoul International Conference on Linguistics (SICOL-2022), Sungkyunkwan University, South Korea., 11-12 August 2022, Online. http://sicol2022.creatorlink.net/
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic P Philology. Linguistics
spellingShingle P Philology. Linguistics
Siti Marina, Kamil
NurAthirah, Zulrushdi
CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
description This study aims to categorize the offensive language identified within the hate speeches produced by K-pop fans on Twitter. It is a concern that the use of hate speeches among the fans would intensify cyberbullying. Nonetheless, little researches have been done on the categorization of offensive languages as previous researches have primarily relied on different data sets thus making it hard to do a comparison of different studies. There have been no benchmark data sets in categorizing offensive languages. A Natural Language Processing toolkit called “Orange” is used to extract tweets within a period with some requirements highlighted. The framework adopted from Wiegand et al. (2018) is used to categorize offensive language in this study. Findings have shown that from the 13,300 tweets that have been streamed, only 469 tweets are identified as hate speeches. Among all the 469 tweets, the insult category tops with 162 tweets, followed by the other category with 150 tweets, the profanity category with 135 tweets, and the abuse category with only 22 tweets. Contrary to what others might have presumed, K-pop fans are always associated with a negative nuance. In the desire to change this, the input in the analysis of hate speeches as well as the categorization of offensive languages gathered from this study has shown that even though the insult category tops the result, this study is not meant to generalize all K-pop fans. A lot of K-pop fans are aware of boundaries seeing that only 22 tweets are identified as abusive. This study will hopefully open up a new beginning to other researchers in this specific study.
format Proceeding
author Siti Marina, Kamil
NurAthirah, Zulrushdi
author_facet Siti Marina, Kamil
NurAthirah, Zulrushdi
author_sort Siti Marina, Kamil
title CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
title_short CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
title_full CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
title_fullStr CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
title_full_unstemmed CLASSIFYING OFFENSIVE LANGUAGE USE IN HATE SPEECHES OF K-POP FANS ON TWITTER
title_sort classifying offensive language use in hate speeches of k-pop fans on twitter
publisher The Linguistic Society of Korea
publishDate 2002
url http://ir.unimas.my/id/eprint/40813/2/cover.pdf
http://ir.unimas.my/id/eprint/40813/3/Classifying%20Offensive.pdf
http://ir.unimas.my/id/eprint/40813/
http://sicol2022.creatorlink.net/
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score 13.154949