|
--- |
|
license: mit |
|
dataset_info: |
|
features: |
|
- name: text |
|
dtype: string |
|
- name: impoliteness |
|
dtype: int64 |
|
- name: intolerance |
|
dtype: int64 |
|
splits: |
|
- name: train |
|
num_bytes: 2169574.4014020115 |
|
num_examples: 10498 |
|
- name: test |
|
num_bytes: 542703.5985979884 |
|
num_examples: 2626 |
|
download_size: 1726706 |
|
dataset_size: 2712278 |
|
task_categories: |
|
- text-classification |
|
language: |
|
- en |
|
--- |
|
|
|
## Overview |
|
The TwitCivility dataset is specifically developed to classify political incivility, focusing on multidimensional aspects of impoliteness and intolerance. |
|
Detailed methodologies are outlined in our [paper](https://arxiv.org/abs/2305.14964). |
|
|
|
|
|
## Languages |
|
All text is written in English. |
|
|
|
## Dataset Structure |
|
### Data Fields |
|
We release TwitCivility as a data frame with the following fields: <br /> |
|
**text**: This field contains the text (after preprocessing and anonymization) of the tweet. <br /> |
|
**impoliteness**: A binary indicator (1 or 0) representing the presence of impoliteness in the text. A value of 1 signifies impoliteness, while 0 indicates non-impoliteness. <br /> |
|
**intolerance**: Similarly, this binary value denotes the presence of intolerance in the text, with 1 indicating intolerance and 0 signifying non-intolerance. <br /> |
|
|
|
## Citation Information |
|
``` |
|
@misc{incivility2023, |
|
title={Detecting Multidimensional Political Incivility on Social Media}, |
|
author={Sagi Pendzel and Nir Lotan and Alon Zoizner and Einat Minkov}, |
|
year={2023}, |
|
eprint={2305.14964}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
