SEACrowd/id_hsd_nofaaulia

There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook.

Languages

ind

Supported Tasks

Sentiment Analysis

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/id_hsd_nofaaulia", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("id_hsd_nofaaulia", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("id_hsd_nofaaulia"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://dl.acm.org/doi/10.1145/3330482.3330491

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Unknown

Citation

If you are using the Id Hsd Nofaaulia dataloader in your work, please cite the following:

@inproceedings{10.1145/3330482.3330491,
author = {Aulia, Nofa and Budi, Indra},
title = {Hate Speech Detection on Indonesian Long Text Documents Using Machine Learning Approach},
year = {2019},
isbn = {9781450361064},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3330482.3330491},
doi = {10.1145/3330482.3330491},
abstract = {Due to the growth of hate speech on social media in recent years, it is important to understand this issue. An automatic hate speech detection system is needed to help to counter this problem. There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook. The experiment showed that the best performance obtained by Support Vector Machine (SVM) as its classifier algorithm using TF-IDF, char quad-gram, word unigram, and lexicon features that yield f1-score of 85%.},
booktitle = {Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence},
pages = {164–169},
numpages = {6},
keywords = {machine learning, SVM, long documents, hate speech detection},
location = {Bali, Indonesia},
series = {ICCAI '19}
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}