---
task_categories:
- text-classification
language:
- en
pretty_name: Offensive Language Identification Dataset
configs:
- config_name: default
  data_files: "*.tsv"
  sep: "\t"
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

The Offensice Language Identification Dataset (OLID) contains 14,100 annotated tweets from Twitter, annotated with three subcategories via crowdsourcing and has been released together with 
the paper [Predicting the Type and Target of Offensive Posts in Social Media](https://arxiv.org/abs/1902.09666). 

Previous datasets mainly focused on detecting specific types of offensive messages (hate speech, cyberbulling, etc.) but did not consider offensive language as a whole. 
This dataset is annoated using a hierarchical annotation with up to 3 labels corresponding to offensive language detection (OFF/NOT), 
automatic categorization of offense types (TIN/UNT) and offense target identification (IND/GRP/OTH), described below. 


## Dataset Details
"The gold labels were assigned taking the agreement of three annotators into consideration. No correction has been carried out on the crowdsourcing annotations.
Twitter user mentions were substituted by @USER and URLs have been substitute by URL.

OLID is annotated using a hierarchical annotation. Each instance contains up to 3 labels each corresponding to one of the following levels:

- Level (or sub-task) A: Offensive language identification; 

- Level (or sub-task) B: Automatic categorization of offense types;

- Level (or sub-task) C: Offense target identification." ([Source](https://github.com/idontflow/OLID?tab=readme-ov-file#readme))

### Tasks and Labels ([Source](https://github.com/idontflow/OLID?tab=readme-ov-file#readme))

(A) Level A: Offensive language identification

- (NOT) Not Offensive - This post does not contain offense or profanity.
- (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense

In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. 

(B) Level B: Automatic categorization of offense types

- (TIN) Targeted Insult and Threats - A post containing an insult or threat to an individual, a group, or others (see categories in sub-task C).
- (UNT) Untargeted - A post containing non-targeted profanity and swearing.

Posts containing general profanity are not targeted, but they contain non-acceptable language.

(C) Level C: Offense target identification

- (IND) Individual - The target of the offensive post is an individual: a famous person, a named individual or an unnamed person interacting in the conversation.
- (GRP) Group - The target of the offensive post is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or something else.
- (OTH) Other – The target of the offensive post does not belong to any of the previous two categories (e.g., an organization, a situation, an event, or an issue)

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->


- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** [More Information Needed]

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** [GitHub Repository](https://github.com/idontflow/OLID)
- **Paper [optional]:** [Predicting the Type and Target of Offensive Posts in Social Media](https://arxiv.org/abs/1902.09666)
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->
The goal of this dataset was 

[More Information Needed]

### Source Data
The data originates from Twitter
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The authors retrieved the samples "from Twitter using its API and searching for keywords and constructions that are often included in 
offensive messages, such as ‘she is’ or ‘to:BreitBartNews’" ([Source](https://arxiv.org/pdf/1902.09666.pdf)). 

They used the following keywords (except for the first three rows)

| Keyword           | Offensive % |
|-------------------|-------------|
| medical marijuana | 0.0         |
| they are          | 5.9         |
| to:NewYorker      | 8.3         |
| ---------         | -----       |
| you are           | 21.0        |
| she is            | 26.6        |
| to:BreitBartNews  | 31.6        |
| he is             | 32.4        |
| gun control       | 34.7        |
| -filter:safe      | 58.9        |
| conservatives     | 23.2        |
| antifa            | 26.7        |
| MAGA              | 27.7        |
| liberals          | 38.0        |


[More Information Needed]

#### Who are the source data producers?

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### Annotations [optional]
Extensive information on this can be found in the [original paper](https://arxiv.org/pdf/1902.09666.pdf) in the Data Collection section. 
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process
The annotation has been executed in a crowdsourcing process, where the gold label has been created by considering the annotations of three different annotators.  
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

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#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
Usernames have been replaced by "USER", URL's by "URL".
[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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