An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

Ibrahim, Muhammad Amien; Arifin, Samsul; Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Abdillah, Abdul Azis; Murnaka, Nerru Pranuta; Prasetyo, Puguh Wahyu

doi:10.21512/commit.v16i2.8343

Cited by 4 publications

(4 citation statements)

References 17 publications

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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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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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“…In addition to this, the experiments in data augmenting and modeling use unplug and scikit-learn libraries respectively. Meanwhile, the visualization uses matplotlib and seaborn libraries (Ibrahim et al, 2022b;Sagala and Ibrahim, 2022).…”

Section: Preprocessingmentioning

confidence: 99%

“…This means that the minority class samples are augmented until it reaches 612 samples. This decision is taken by considering that the minority class samples should be augmented at a level where the variation in the augmentation would not reduce the meaning of the samples (Ibrahim et al, 2022b).…”

Section: Data Augmentationmentioning

confidence: 99%

“…The parameters of these models use default parameters as defined in the sci-kit-learn library. Four distinct algorithms are experimented which were chosen based on prior research (Ibrahim et al, 2022b). Logistic regression is a statistical approach used to predict binary categories by analyzing the likelihood of an event occurring (Plaza-Del-Arco et al, 2020).…”

Section: Modelingmentioning

confidence: 99%

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Exploring Data Augmentation for Gender-Based Hate Speech Detection

Ibrahim,

Arifin,

Purwanto

2023

Journal of Computer Science

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Social media moderation is a crucial component to establish healthy online communities and ensuring online safety from hate speech and offensive language. In many cases, hate speech may be targeted at specific gender which could be expressed in many different languages on social media platforms such as Indonesian Twitter. However, difficulties such as data scarcity and the imbalanced gender-based hate speech dataset in Indonesian tweets have slowed the development and implementation of automatic social media moderation. Obtaining more data to increase the number of samples may be costly in terms of resources required to gather and annotate the data. This study looks at the usage of data augmentation methods to increase the amount of textual dataset while keeping the quality of the augmented data. Three augmentation strategies are explored in this study: Random insertion, back translation, and a sequential combination of back translation and random insertion. Additionally, the study examines the preservation of the increased data labels. The performance result demonstrates that classification models trained with augmented data generated from random insertion strategy outperform the other approaches. In terms of label preservation, the three augmentation approaches have been shown to offer enough label preservation without compromising the meaning of the augmented data. The findings imply that by increasing the amount of the dataset while preserving the original label, data augmentation could be utilized to solve issues such as data scarcity and dataset imbalance.

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Explainable Artificial Intelligence for Combating Cyberbullying

Tesfagergish,

Damaševičius

2024

Communications in Computer and Information Science

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An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

Cited by 4 publications

References 17 publications

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Exploring Data Augmentation for Gender-Based Hate Speech Detection

Explainable Artificial Intelligence for Combating Cyberbullying

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