Hate Code Detection in Indonesian Tweets using Machine Learning Approach: A Dataset and Preliminary Study

Elisabeth, Damayanti; Budi, Indra; Ibrohim, Muhammad Okky

doi:10.1109/icoict49345.2020.9166251

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…Moreover, some studies focused on techniques to deal with the class imbalance problem, such as oversampling and undersampling. The oversampling technique is applied in the training data to increase the minority class (Chatzakou et al, 2017;Elisabeth et al, 2020), while the undersampling technique reduces the majority class (Miok et al, 2019). However, most of the works did not deal with class imbalance.…”

Section: Automatic Hate Speech Detectionmentioning

confidence: 99%

“…In Plaza-Del-Arco et al ( 2020) used TF weighting to represent unigrams and bigrams as vectors of numerical features to misogyny and xenophobia detected in Spanish tweets. Several works used TF-IDF weighting features for hate speech detection (Almatarneh et al, 2019;Elisabeth et al, 2020;Mossie & Wang, 2020;Salminen et al, 2020). The TF-IDF provided good classification performance for hate speech detection with the same dataset to train and test the models.…”

Section: Term Frequencymentioning

confidence: 99%

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Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

2023

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Hate speech is a challenging problem, and its dissemination can cause potential harm to individuals and society by creating a sense of general unwelcoming to the marginalized groups, which usually are targeted. Therefore, it is essential to understand this issue and which techniques are useful for automatic detection. This paper presents a survey on automatic hate speech detection on social media, providing a structured overview of theoretical aspects and practical resources. Thus, we review different definitions of the term “hate speech” from social network platforms and the scientific community. We also present an overview of the methodologies used for hate speech detection, and we describe the main approaches currently explored in this context, including popular features, datasets, and algorithms. Furthermore, we discuss some challenges and opportunities for better solving this issue.

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Section: Automatic Hate Speech Detectionmentioning

confidence: 99%

Section: Term Frequencymentioning

confidence: 99%

Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

2023

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“…We carefully reviewed each document to obtain the key information of each work. In this part, we focus on [11], [30], [17], [23], [12], [28], [27], [21], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]…”

Section: B What Has Been Done So Far In Indonesian Abusive Language D...mentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

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This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data.

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“…General feature representation methods of text mining have been successfully adapted to the problem of hate speech detection, such as Bag-of-Words (BoW) (Burnap & Williams, 2016;Nobata et al, 2016), n-grams (Corazza et al, 2020;Santosh & Aravind, 2019), dictionaries or lexical resources (Gitari et al, 2015;Mathew et al, 2019), etc. Regarding classification perspective, different algorithms have been employed, such as Logistic Regression (Davidson et al, 2017), Support Vector Machine (SVM) (Salminen et al, 2018), Random Forest (Elisabeth et al, 2020), Decision tree (Plaza-Del-Arco et al, 2020). Davidson et al (2017) addressed the problem of hate speech detection on Twitter, focusing on distinguishing between hate speech and offensive language.…”

Section: Automatic Hate Speech Detectionmentioning

confidence: 99%

“…For the representations f 4 to f 9 , we selected traditional feature extraction methods used for hate speech detection (Almatarneh et al, 2019;Corazza et al, 2020;Elisabeth et al, 2020;Salminen et al, 2020;Senarath & Purohit, 2020;Santosh & Aravind, 2019). These methods are based on the Bag-of-Words (BoW) technique.…”

Section: Pool Generationmentioning

confidence: 99%

Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning

Nascimento¹,

Cavalcanti²,

Abreu³

2022

Expert Systems with Applications

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Hate Code Detection in Indonesian Tweets using Machine Learning Approach: A Dataset and Preliminary Study

Cited by 18 publications

References 7 publications

Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning

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