---
language:
- en
license: cc-by-4.0
task_categories:
- text-classification
tags:
- journalism
pretty_name: 'Antisemitism on Twitter: A Dataset for Machine Learning and Text Analytics'
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset on Antisemitism on Twitter/X

## Dataset Description

- **Homepage:** 
- **Repository:** https://zenodo.org/record/7932888
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

The ISCA project has compiled this dataset using an annotation portal, which was used to label tweets as either antisemitic or non-antisemitic, among other labels. Please note that the annotation was done with live data, including images and the context, such as threads. The original data was sourced from annotationportal.com.


### Languages

English

## Dataset Structure

‘TweetID’: Represents the tweet ID. 

‘Username’: Represents the username who published the tweet.  

‘Text’: Represents the full text of the tweet (not pre-processed).

‘CreateDate’: Represents the date the tweet was created.  

‘Biased’: Represents the labeled by our annotations if the tweet is antisemitic or non-antisemitic. 

‘Keyword’: Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.  


## Dataset Creation

This dataset contains 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021. The dataset is drawn from representative samples during this period with relevant keywords. 1,250 tweets (18%) meet the IHRA definition of antisemitic messages.  

The dataset has been compiled within the ISCA project using an annotation portal to label tweets as either antisemitic or non-antisemitic. The original data was sourced from annotationportal.com. 


### Annotations

#### Annotation process

We annotated the tweets, considering the text, images, videos, and links, in their “natural” context, including threads. We used a detailed annotation guideline, based on the IHRA Definition, which has been endorsed and recommended by more than 30 governments and international organizations5 and is frequently used to monitor and record antisemitic incidents. We divided the definition into 12 paragraphs. Each of the paragraphs addresses different forms and tropes of antisemitism. We created an online annotation tool (https://annotationportal.com) to make labeling easier, more consistent, and less prone to errors, including in the process of recording the annotations. The portal displays the tweet and a clickable annotation form, see Figure 1. It automatically saves each annotation, including the time spent labeling each tweet.
The Annotation Portal retrieves live tweets by referencing their ID number. Our annotators first look at the tweet, and if they are unsure of the meaning, they are prompted to look at the entire thread, replies, likes, links, and comments. A click on the visualized tweet opens a new tab in the browser, displaying the message on the Twitter page in its “natural” environment.
The portal is designed to help annotators consistently label messages as antisemitic or not according to the IHRA definition. After verifying that the message is still live and in English, they select from a drop-down menu where they classify the message as "confident antisemitic," "probably antisemitic," "probably not antisemitic," "confident not antisemitic," or "don’t know." The annotation guideline, including the definition, is linked in a PDF document.

#### Who are the annotators?
All annotators are familiar with the definition and have been trained on test samples. They have also taken at least one academic course on antisemitism or have done research on antisemitism. We consider them to be expert annotators. Eight such expert annotators of different religions and genders labeled the 18 samples, two for each sample in alternating configurations.

## Considerations for Using the Data

### Social Impact of Dataset

One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets.
We focus on antisemitic speech on Twitter and create a labeled dataset of 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021 by drawing from representative samples with relevant keywords.
Our annotation process aims to strictly apply a commonly used definition of antisemitism by forcing annotators to specify which part of the definition applies, and by giving them the option to personally disagree with the definition on a case-by-case basis. Labeling tweets that call out antisemitism, report antisemitism, or are otherwise related to antisemitism (such as the Holocaust) but are not actually antisemitic can help reduce false positives in automated detection.

## Additional Information

### Dataset Curators

Gunther Jikeli, Sameer Karali, Daniel Miehling, and Katharina Soemer


### Citation Information

Jikeli,Gunther, Sameer Karali, Daniel Miehling, and Katharina Soemer (2023): Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets. https://arxiv.org/abs/2304.14599