Synonym based feature expansion for Indonesian hate speech detection

Ghozali, Imam; Sungkono, Kelly Rossa; Sarno, Riyanarto; Abdullah, Rachmad

doi:10.11591/ijece.v13i1.pp1105-1112

Cited by 7 publications

(4 citation statements)

References 24 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recall is the success rating of the system in rediscovering information [31]. The F1-score is the sync mean of recall and precision [32].…”

Section: Discussionmentioning

confidence: 99%

Improving cyberbullying detection through multi-level machine learning

Salsabila,

Sarno,

Ghozali

et al. 2024

IJECE

View full text Add to dashboard Cite

Cyberbullying is a known risk factor for mental health issues, demanding immediate attention. This study aims to detect cyberbullying on social media in alignment with the third sustainable development goal (SDG) for health and well-being. Many previous studies employ single-level classification, but this research introduces a multi-class multi-level (MCML) algorithm for a more detailed approach. The MCML approach incorporates two levels of classification: level one for cyberbullying or not cyberbullying, and level two for classifying cyberbullying by type. This study used a dataset of 47,000 tweets from Twitter with six class labels and employed an 80:20 training and testing data split. By integrating bidirectional encoder representations from transformers (BERT) and MCML at level two, we achieved a remarkable 99% accuracy, surpassing BERT-based single-level classification at 94%. In conclusion, the combination of MCML and BERT offers enhanced cyberbullying classification accuracy, contributing to the broader goal of promoting mental health and well-being.

show abstract

“…The recall is the success rating of the system in rediscovering information [31]. The F1-score is the sync mean of recall and precision [32].…”

Section: Discussionmentioning

confidence: 99%

Improving cyberbullying detection through multi-level machine learning

Salsabila,

Sarno,

Ghozali

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

“…Additionally, hate speech can elicit strong emotional reactions and have a depressing impact on social self-esteem, which has an impact on people's psychological health, especially those who are the target of it (Boeckmann & Liew, 2002). The rise of hate speech in Indonesia, facilitated by the development of internet-based social media, has negative implications for online interactions and societal harmony (Ghozali et al, 2023). The experience of hostility, hate speech, and adversarial behavior in online spaces has increased substantially, with documented health and mental health outcomes for users (Cover, 2022).…”

Section: Factors Caused Hate Speech In Social Media In Indonesiamentioning

confidence: 99%

Empathy and Hate Speech in Social Media: The Case of Indonesia

Rahmi

2024

IJSSHR

View full text Add to dashboard Cite

Hate speech on social media in Indonesia 10 years ago has become a very interesting topic of discussion. Many of these incidents occur and are perpetrated by teenagers, with implications for violence and their psychological well-being. Hate speech on social media, particularly in the Indonesian context, presents a multifaceted challenge that requires a combination of technological solutions, legal frameworks, and community interventions to effectively combat its spread and impact. By utilizing insights from research on hate speech detection, empathy promotion, and regulatory frameworks, stakeholders can work towards creating a safer and more inclusive online environment in Indonesia. Strengthening digital literacy, as suggested in the references, is essential to empower individuals to identify and respond effectively to hate speech. Legal measures, as emphasized in the references, play an important role in combating hate speech, with the establishment of specialized units such as the Indonesian Virtual Police contributing to monitoring and handling hate speech incidents. Community engagement and educational programs, as discussed in the references, are crucial in promoting dialogue, tolerance, and understanding to foster a culture of mutual respect and empathy.

show abstract

“…[26], [19], [13], [44], [45], [46], [47], [48], [49], [50], [51], [52] Transformer-based Models Using the recent transformer based architecture such as BERT, RoBERTa, XLM, and etc. Based on the previous studies in NLP area, these models usually provide the robust performance across different NLP tasks.…”

Section: Unsupervised Approachmentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Synonym based feature expansion for Indonesian hate speech detection

Cited by 7 publications

References 24 publications

Improving cyberbullying detection through multi-level machine learning

Improving cyberbullying detection through multi-level machine learning

Empathy and Hate Speech in Social Media: The Case of Indonesia

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Contact Info

Product

Resources

About