Datasets:
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English
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"""Ethos dataset"""
import pandas as pd
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@misc{mollas2020ethos,
title={ETHOS: an Online Hate Speech Detection Dataset},
author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas},
year={2020},
eprint={2006.08328},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
detection on social media platforms, called Ethos. There are two variations of the dataset:
Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
about hate speech presence or absence. 565 of them do not contain hate speech,
while the rest of them, 433, contain.
Ethos_Dataset_Multi_Label: which contains 8 labels for the 433 comments with hate speech content.
These labels are violence (if it incites (1) or not (0) violence), directed_vs_general (if it is
directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like,
gender, race, national_origin, disability, religion and sexual_orientation.
"""
_URL = "https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
class EthosConfig(datasets.BuilderConfig):
"""BuilderConfig for Ethos."""
def __init__(self, variation="binary", **kwargs):
"""Constructs an EthosDataset.
Args:
variation: can be binary or multilabel
**kwargs: keyword arguments forwarded to super.
"""
if variation.lower() == "binary":
self.variation = "binary"
elif variation.lower() == "multilabel":
self.variation = "multilabel"
else:
logger.warning("Wrong variation. Could be either 'binary' or 'multilabel', using 'binary' instead.")
self.variation = "binary"
super(EthosConfig, self).__init__(**kwargs)
class Ethos(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = EthosConfig
BUILDER_CONFIGS = [
EthosConfig(
name="binary",
version=datasets.Version("1.0.0", ""),
description="Ethos Binary",
variation="binary",
),
EthosConfig(
name="multilabel",
version=datasets.Version("1.0.0", ""),
description="Ethos Multi Label",
variation="multilabel",
),
]
def _info(self):
if self.config.variation == "binary":
f = datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["no_hate_speech", "hate_speech"]),
}
)
else:
f = datasets.Features(
{
"text": datasets.Value("string"),
"violence": datasets.ClassLabel(names=["not_violent", "violent"]),
"directed_vs_generalized": datasets.ClassLabel(names=["generalied", "directed"]),
"gender": datasets.ClassLabel(names=["false", "true"]),
"race": datasets.ClassLabel(names=["false", "true"]),
"national_origin": datasets.ClassLabel(names=["false", "true"]),
"disability": datasets.ClassLabel(names=["false", "true"]),
"religion": datasets.ClassLabel(names=["false", "true"]),
"sexual_orientation": datasets.ClassLabel(names=["false", "true"]),
}
)
return datasets.DatasetInfo(
features=f,
supervised_keys=None,
homepage="https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset/tree/masterethos/ethos_data",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.variation == "binary":
url = {
"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
"/master/ethos/ethos_data/Ethos_Dataset_Binary.csv"
}
else:
url = {
"train": "https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset"
"/master/ethos/ethos_data/Ethos_Dataset_Multi_Label.csv"
}
downloaded_files = dl_manager.download_and_extract(url)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]
def _generate_examples(self, filepath):
"""Yields examples."""
data = pd.read_csv(filepath, delimiter=";")
if self.config.variation == "binary":
x = data["comment"].values
y = [1 if i >= 0.5 else 0 for i in data["isHate"].values]
class_names = ["no_hate_speech", "hate_speech"]
for i in range(len(x)):
_id = i
yield _id, {"text": x[i], "label": class_names[y[i]]}
else:
x = data["comment"].values
y_temp = data.loc[:, data.columns != "comment"].values
y = []
for yt in y_temp:
yi = []
for i in yt:
if i >= 0.5:
yi.append(int(1))
else:
yi.append(int(0))
y.append(yi)
for i in range(len(x)):
_id = i
yield _id, {
"text": x[i],
"violence": y[i][0],
"directed_vs_generalized": y[i][1],
"gender": y[i][2],
"race": y[i][3],
"national_origin": y[i][4],
"disability": y[i][5],
"religion": y[i][6],
"sexual_orientation": y[i][7],
}
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