Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: found
Source Datasets: original

Dataset Card for [Dataset Name]

Dataset Summary

An annotated dataset for hate speech and offensive language detection on tweets.

Supported Tasks and Leaderboards

[More Information Needed]


English (en)

Dataset Structure

Data Instances

"count": 3,
 "hate_speech_annotation": 0,
 "offensive_language_annotation": 0,
 "neither_annotation": 3,
 "label": 2,  # "neither"
 "tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. &amp; as a man you should always take the trash out...")

Data Fields

count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable,
hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech,
offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive,
neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive,
label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'),
tweet: (string)

Data Splits

This dataset is not splitted, only the train split is available.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

Usernames are not anonymized in the dataset.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

MIT License

Citation Information

@inproceedings{hateoffensive, title = {Automated Hate Speech Detection and the Problem of Offensive Language}, author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar}, booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media}, series = {ICWSM '17}, year = {2017}, location = {Montreal, Canada}, pages = {512-515} }


Thanks to @hugoabonizio for adding this dataset.

Models trained or fine-tuned on hate_speech_offensive

None yet