Datasets:
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
languages:
- en
licenses:
- gpl-3-0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
Dataset Card for [Dataset Name]
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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
[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 specch, 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