Hate speech detection in the Indonesian language: A dataset and preliminary study

Alfina, Ika; Mulia, R Marojahan; Fanany, Mohamad Ivan; Ekanata, Yudo

doi:10.1109/icacsis.2017.8355039

Cited by 154 publications

(141 citation statements)

References 4 publications

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“…The first phase annotation process was done to annotate the Twitter data whether tweets are hate speech and abusive language or not, while the second phase annotation process was done to annotate the hate speech target, categories, and level. For tweets from (Alfina et al, 2017(Alfina et al, , 2018 and (Putri, 2018), tweets were just annotated to determine whether the tweet is an abusive language or not in the first phase annotation process, since the hate speech label is already obtained. Meanwhile, tweets from (Ibrohim and Budi, 2018) can be annotated directly in the second phase since their dataset was annotated for hate speech and abusive labels.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

“…In the first annotation phase, we collect 16,500 tweets from the crawling process and previous researches (Alfina et al, 2017(Alfina et al, , 2018Putri, 2018) to be annotated by those 30 annotators. Every tweet was annotated by 3 annotators and the fi- 8 Master in sociolinguistics nal label was decided using 100% agreement technique.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

“…The hate speech and abusive language on social media must be detected to avoid conflicts between citizens and children learning the hate speech and inappropriate language from the social media they use (Komnas HAM, 2015;Chen et al, 2012). In recent years, many researchers have done research in hate speech detection (Waseem and Hovy, 2016;Alfina et al, 2017Alfina et al, , 2018Putri, 2018;Vigna et al, 2017) and abusive language detection (Turaob and Mitrpanont, 2017;Chen et al, 2012;Nobata et al, 2016; in various social media genres and languages.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we do research on hate speech and abusive language detection in Indonesian Twitter. We chose Twitter as our dataset because Twitter is one of the social media platforms in Indonesia that is often used to spread the hate speech and abusive language (Alfina et al, 2017(Alfina et al, , 2018Putri, 2018;. This problem is a multi-label text classification problem, where a tweet can be no hate speech, no hate speech but abusive, hate speech but no abusive, and hate speech and abusive.…”

Section: Introductionmentioning

confidence: 99%

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Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

155

122

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Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflicts between citizens. Moreover, hate speech has a target, category, and level that also need to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approaches with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general the RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.

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Section: Data Collection and Annotationmentioning

confidence: 99%

Section: Data Collection and Annotationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

155

122

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“…Their result showed that the classifiers were not able to discriminate between three levels of hate. In addition, Alfina et al [52] investigated the ability to detect hate in Indonesian language. They took advantage of a political event to collect their sample "Jakarta Governor election 2017".…”

Section: Hate Speech Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

115

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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