File size: 9,356 Bytes
f8dec51
 
 
 
 
14e8cc4
 
64f53f6
f8dec51
64f53f6
 
f8dec51
 
 
 
 
 
 
 
 
d576734
 
2d28c3f
3e1a085
d576734
 
 
d60c63c
 
06e068d
 
 
 
 
 
 
 
 
8d152d1
 
06e068d
 
 
 
 
 
 
 
 
 
 
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
8d152d1
 
06e068d
 
 
 
 
 
f8dec51
 
 
 
 
 
 
2d28c3f
f8dec51
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6a1a2cc
f8dec51
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6a1a2cc
 
d60c63c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
- other
language:
- en
license:
- agpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
- sentiment-classification
paperswithcode_id: ethos
pretty_name: onlinE haTe speecH detectiOn dataSet
configs:
- binary
- multilabel
tags:
- Hate Speech Detection
dataset_info:
- config_name: binary
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': no_hate_speech
          '1': hate_speech
  splits:
  - name: train
    num_bytes: 124823
    num_examples: 998
  download_size: 123919
  dataset_size: 124823
- config_name: multilabel
  features:
  - name: text
    dtype: string
  - name: violence
    dtype:
      class_label:
        names:
          '0': not_violent
          '1': violent
  - name: directed_vs_generalized
    dtype:
      class_label:
        names:
          '0': generalied
          '1': directed
  - name: gender
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: race
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: national_origin
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: disability
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: religion
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: sexual_orientation
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  splits:
  - name: train
    num_bytes: 79112
    num_examples: 433
  download_size: 62836
  dataset_size: 79112
---

# Dataset Card for Ethos

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [ETHOS Hate Speech Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset)
- **Repository:**[ETHOS Hate Speech Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset)
- **Paper:**[ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328)

### Dataset Summary

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*.

***Ethos /ˈiːθɒs/*** 
is a Greek word meaning “character” that is used to describe the guiding beliefs or ideals that characterize a community, nation, or ideology. The Greeks also used this word to refer to the power of music to influence emotions, behaviors, and even morals.

### Supported Tasks and Leaderboards

[More Information Needed]
- `text-classification-other-Hate Speech Detection`, `sentiment-classification`,`multi-label-classification`: The dataset can be used to train a model for hate speech detection. Moreover, it can be used as a benchmark dataset for multi label classification algorithms.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

A typical data point in the binary version comprises a comment, with a `text` containing the  text and a `label` describing if a comment contains hate speech content (1 - hate-speech) or not (0 - non-hate-speech). In the multilabel version more labels like *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* are appearing.

An example from the binary version, which is offensive, but it does not contain hate speech content:
```
{'text': 'What the fuck stupid people !!!',
 'label': '0'
}
```

An example from the multi-label version, which contains hate speech content towards women (gender):
```
{'text': 'You should know women's sports are a joke',
 `violence`: 0,
 `directed_vs_generalized`: 0,
 `gender`: 1,
 `race`: 0,
 `national_origin`: 0,
 `disability`: 0,
 `religion`: 0,
 `sexual_orientation`: 0
}
```


### Data Fields

Ethos Binary:
- `text`: a `string` feature containing the text of the comment.
- `label`: a classification label, with possible values including `no_hate_speech`, `hate_speech`.

Ethis Multilabel:
- `text`: a `string` feature containing the text of the comment.
- `violence`: a classification label, with possible values including `not_violent`, `violent`.
- `directed_vs_generalized`: a classification label, with possible values including `generalized`, `directed`.
- `gender`: a classification label, with possible values including `false`, `true`.
- `race`: a classification label, with possible values including `false`, `true`.
- `national_origin`: a classification label, with possible values including `false`, `true`.
- `disability`: a classification label, with possible values including `false`, `true`.
- `religion`: a classification label, with possible values including `false`, `true`.
- `sexual_orientation`: a classification label, with possible values including `false`, `true`.

### Data Splits

The data is split into binary and multilabel. Multilabel is a subset of the binary version.

|                             | Instances   | Labels |
| -----                       | ------ | ----- |
| binary | 998 |  1 |
| multilabel       | 433 |  8 |

## Dataset Creation

### Curation Rationale

The dataset was build by gathering online comments in Youtube videos and reddit comments, from videos and subreddits which may attract hate speech content. 

### Source Data

#### Initial Data Collection and Normalization

The initial data we used are from the hatebusters platform: [Original data used](https://intelligence.csd.auth.gr/topics/hate-speech-detection/), but they were not included in this dataset

#### Who are the source language producers?

The language producers are users of reddit and Youtube. More informations can be found in this paper: [ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328)

### Annotations

#### Annotation process

The annotation process is detailed in the third section of this paper: [ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328)

#### Who are the annotators?

Originally anotated by Ioannis Mollas and validated through the Figure8 platform (APEN).

### Personal and Sensitive Information

No personal and sensitive information included in the dataset.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset will help on the evolution of the automated hate speech detection tools. Those tools have great impact on preventing social issues.

### Discussion of Biases

This dataset tries to be unbiased towards its classes and labels.

### Other Known Limitations

The dataset is relatively small and should be used combined with larger datasets.

## Additional Information

### Dataset Curators

The dataset was initially created by [Intelligent Systems Lab](https://intelligence.csd.auth.gr).

### Licensing Information

The licensing status of the datasets is [GNU GPLv3](https://choosealicense.com/licenses/gpl-3.0/).

### Citation Information
```
@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}
}
```

### Contributions

Thanks to [@iamollas](https://github.com/iamollas) for adding this dataset.