Datasets:
tweets_hate_speech_detection

	---
	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
	---

	# 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.