SEACrowd/uit_vihsd

The ViHSD dataset consists of comments collected from Facebook pages and YouTube channels that have a high-interactive rate, and do not restrict comments. This dataset is used for hate speech detection on Vietnamese language. Data is anonymized, and labeled as either HATE, OFFENSIVE, or CLEAN.

Languages

vie

Supported Tasks

Sentiment Analysis

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("uit_vihsd", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("uit_vihsd"))
# 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://github.com/sonlam1102/vihsd/

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Unknown (unknown)

Citation

If you are using the Uit Vihsd dataloader in your work, please cite the following:

@InProceedings{10.1007/978-3-030-79457-6_35,
author="Luu, Son T.
and Nguyen, Kiet Van
and Nguyen, Ngan Luu-Thuy",
editor="Fujita, Hamido
and Selamat, Ali
and Lin, Jerry Chun-Wei
and Ali, Moonis",
title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="415--426",
abstract="In recent years, Vietnam witnesses the mass development of social network users on different social
platforms such as Facebook, Youtube, Instagram, and Tiktok. On social media, hate speech has become a critical
problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for
automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment
in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process
for annotating and evaluating the quality of the dataset. Finally, we evaluate the dataset by deep learning and transformer models.",
isbn="978-3-030-79457-6"
}


@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}
}