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--- |
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task_categories: |
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- text-classification |
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language: |
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- en |
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task_ids: |
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- sentiment-classification |
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- hate-speech-detection |
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size_categories: |
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- 1K<n<10K |
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--- |
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(to be updated...) |
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## Description |
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**Tweet Annotation Sensitivity Experiment 1** |
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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. |
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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. |
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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). |
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## Citation |
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If you found the dataset useful, please cite: |
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``` |
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@InProceedings{beck2022, |
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author="Beck, Jacob and Eckman, Stephanie and Chew, Rob and Kreuter, Frauke", |
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editor="Chen, Jessie Y. C. and Fragomeni, Gino and Degen, Helmut and Ntoa, Stavroula", |
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title="Improving Labeling Through Social Science Insights: Results and Research Agenda", |
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booktitle="HCI International 2022 -- Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence", |
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year="2022", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="245--261", |
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isbn="978-3-031-21707-4" |
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} |
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``` |
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