Datasets:
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---
task_categories:
- text-classification
language:
- en
task_ids:
- sentiment-classification
- hate-speech-detection
size_categories:
- 1K<n<10K
---
(to be updated...)
## Description
**Tweet Annotation Sensitivity Experiment 1**
We drew a stratified sample of 20 tweets, that were pre-annotated in a study by [Davidson et al. (2017)](https://ojs.aaai.org/index.php/ICWSM/article/view/14955) for Hate Speech / Offensive Language / Neither. The stratification was done with respect to majority-voted class and level of disagreement.
We then recruited 1000 [Prolific](https://www.prolific.com/) workers to annotate each of the 20 tweets. Annotators were randomly selected into one of six experimental conditions. In these conditions they were asked to assign the labels Hate Speech / Offensive Language / Neither.
In addition, we collected a variety of demographic variables (e.g. age and gender) and some para data (e.g. duration of the whole task, duration per screen).
## Citation
If you found the dataset useful, please cite:
```
@InProceedings{beck2022,
author="Beck, Jacob and Eckman, Stephanie and Chew, Rob and Kreuter, Frauke",
editor="Chen, Jessie Y. C. and Fragomeni, Gino and Degen, Helmut and Ntoa, Stavroula",
title="Improving Labeling Through Social Science Insights: Results and Research Agenda",
booktitle="HCI International 2022 -- Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="245--261",
isbn="978-3-031-21707-4"
}
```
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