Datasets:

soda-lmu
/

tweet-annotation-sensitivity-1

Tasks:

Text Classification

Modalities:

Formats:

Sub-tasks:

sentiment-classification

hate-speech-detection

Languages:

Size:

Libraries:

Dataset card Viewer Files Files and versions Community

boleima commited on Oct 20, 2023

Commit

b826534

·

1 Parent(s): 2e24e9c

Update README.md

Files changed (1) hide show

README.md +1 -3

README.md CHANGED Viewed

@@ -9,12 +9,10 @@ task_ids:
 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.

 size_categories:
 - 1K<n<10K
 ---
+# Tweet Annotation Sensitivity Experiment 1
 ## Description
 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.