# Dataset: tweets_hate_speech_detection

Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original

# Dataset Card for [Dataset Name]

### 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.

### 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

### Source Data

#### Initial Data Collection and Normalization

Crowdsourced from tweets of users

### 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

## 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

Roshan Sharma

Information

Citation

### Contributions

Thanks to @darshan-gandhi for adding this dataset.

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