
# Dataset Card for Tweets Hate Speech Detection

## 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:** [Home](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis)
- **Repository:** [Repo](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/train_tweet.csv)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com)

### Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages
The tweets are primarily in English Language.

## Dataset Structure

### Data Instances

The dataset contains a label denoting is the tweet a hate speech or not

```
{'label': 0,  # not a hate speech
 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run'}
```


### Data Fields

* label : 1 - it is a hate speech, 0 - not a hate speech.
* tweet: content of the tweet as a string.

### Data Splits

The data contains training data with :31962 entries

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

Crowdsourced from tweets of users

#### Who are the source language producers?

Cwodsourced from twitter

### Annotations

#### Annotation process

The data has been precprocessed and a model has been trained to assign the relevant label to the tweet

#### Who are the annotators?

The data has been provided by Roshan Sharma

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of   hatred/racist comments

### Discussion of Biases

The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques


[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

Roshan Sharma

### Licensing Information

[Information](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/LICENSE)

### Citation Information

[Citation](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/CONTRIBUTING.md)

### Contributions

Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:gpl-3.0 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["gpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Tweets Hate Speech Detection", "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "no-hate-speech", "1": "hate-speech"}}}}, {"name": "tweet", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3191888, "num_examples": 31962}, {"name": "test", "num_bytes": 1711606, "num_examples": 17197}], "download_size": 4738708, "dataset_size": 4903494}, "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train"}, "col_mapping": {"tweet": "text", "label": "target", "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 binary", "args": {"average": "binary"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}}]}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-gpl-3.0 #region-us 


# Dataset Card for Tweets Hate Speech Detection

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: Home
- Repository: Repo
- Paper:
- Leaderboard:
- Point of Contact: Darshan Gandhi

### Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.

### Supported Tasks and Leaderboards



### Languages
The tweets are primarily in English Language.

## Dataset Structure

### Data Instances

The dataset contains a label denoting is the tweet a hate speech or not




### Data Fields

* label : 1 - it is a hate speech, 0 - not a hate speech.
* tweet: content of the tweet as a string.

### Data Splits

The data contains training data with :31962 entries

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization

Crowdsourced from tweets of users

#### Who are the source language producers?

Cwodsourced from twitter

### Annotations

#### Annotation process

The data has been precprocessed and a model has been trained to assign the relevant label to the tweet

#### Who are the annotators?

The data has been provided by Roshan Sharma

### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset

With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of   hatred/racist comments

### Discussion of Biases

The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques




### Other Known Limitations



## Additional Information

### Dataset Curators

Roshan Sharma

### Licensing Information

Information



Citation

### Contributions

Thanks to @darshan-gandhi for adding this dataset.
# Dataset Card for Tweets Hate Speech Detection ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: Home
- Repository: Repo
- Paper:
- Leaderboard:
- Point of Contact: Darshan Gandhi ### Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset. ### Supported Tasks and Leaderboards ### Languages
The tweets are primarily in English Language. ## Dataset Structure ### Data Instances

The dataset contains a label denoting is the tweet a hate speech or not ### Data Fields

* label : 1 - it is a hate speech, 0 - not a hate speech.
* tweet: content of the tweet as a string. ### Data Splits

The data contains training data with :31962 entries ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization

Crowdsourced from tweets of users #### Who are the source language producers?

Cwodsourced from twitter ### Annotations #### Annotation process

The data has been precprocessed and a model has been trained to assign the relevant label to the tweet #### Who are the annotators?

The data has been provided by Roshan Sharma ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset

With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of   hatred/racist comments ### Discussion of Biases

The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques ### Other Known Limitations ## Additional Information ### Dataset Curators

Roshan Sharma ### Licensing Information

Information



Citation ### Contributions

Thanks to @darshan-gandhi for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-gpl-3.0 #region-us 
 # Dataset Card for Tweets Hate Speech Detection ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: Home
- Repository: Repo
- Paper:
- Leaderboard:
- Point of Contact: Darshan Gandhi ### Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset. ### Supported Tasks and Leaderboards ### Languages
The tweets are primarily in English Language. ## Dataset Structure ### Data Instances

The dataset contains a label denoting is the tweet a hate speech or not ### Data Fields

* label : 1 - it is a hate speech, 0 - not a hate speech.
* tweet: content of the tweet as a string. ### Data Splits

The data contains training data with :31962 entries ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization

Crowdsourced from tweets of users #### Who are the source language producers?

Cwodsourced from twitter ### Annotations #### Annotation process

The data has been precprocessed and a model has been trained to assign the relevant label to the tweet #### Who are the annotators?

The data has been provided by Roshan Sharma ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset

With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of   hatred/racist comments ### Discussion of Biases

The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques ### Other Known Limitations ## Additional Information ### Dataset Curators

Roshan Sharma ### Licensing Information

Information



Citation ### Contributions

Thanks to @darshan-gandhi for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-gpl-3.0 #region-us 
# Dataset Card for Tweets Hate Speech Detection## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: Home
- Repository: Repo
- Paper:
- Leaderboard:
- Point of Contact: Darshan Gandhi### Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.### Supported Tasks and Leaderboards### Languages
The tweets are primarily in English Language.## Dataset Structure### Data Instances

The dataset contains a label denoting is the tweet a hate speech or not### Data Fields

* label : 1 - it is a hate speech, 0 - not a hate speech.
* tweet: content of the tweet as a string.### Data Splits

The data contains training data with :31962 entries## Dataset Creation### Curation Rationale### Source Data