Update README.md

README.md

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+## 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{becketal_22,
+      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"
+}
+```