Datasets:
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 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 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
Citation Information
Contributions
Thanks to @darshan-gandhi for adding this dataset.