Datasets:
Tasks:
Text Classification
Languages:
English
Size:
n<1K
ArXiv:
Tags:
Hate Speech Detection
License:
"""Ethos dataset""" | |
from __future__ import absolute_import, division, print_function | |
import logging | |
import pandas as pd | |
import datasets | |
_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: | |
logging.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], | |
} | |