# Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives -- an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19

arXiv: https://arxiv.org/abs/2210.07934 · HTML: https://mlsanity.com/paper/2210.07934/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 78 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism](https://mlsanity.com/paper/1910.01214/) (2019)
- [Online antisemitism across platforms](https://mlsanity.com/paper/2112.07783/) (2021)

## Built on by (most cited first, 6 of 6)

- [Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets](https://mlsanity.com/paper/2304.14599/) (2023)
- [How toxic is antisemitism? Potentials and limitations of automated toxicity scoring for antisemitic online content](https://mlsanity.com/paper/2310.04465/) (2023)
- [Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models](https://mlsanity.com/paper/2404.17985/) (2024)
- [Evaluating Large Language Models for Antisemitic Incident Classification](https://mlsanity.com/paper/2607.04890/) (2026)
- [You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism](https://mlsanity.com/paper/2607.04945/) (2026)
- [Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts](https://mlsanity.com/paper/2607.04962/) (2026)

## Abstract (arXiv)

Over the course of the COVID-19 pandemic, existing conspiracy theories were refreshed and new ones were created, often interwoven with antisemitic narratives, stereotypes and codes. The sheer volume of antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential for anti-discrimination organizations and researchers alike. However, the manifestation and dissemination of these two interrelated phenomena is still quite under-researched in scholarly empirical research of large text corpora. Algorithmic approaches for the detection and classification of specific contents usually require labeled datasets, annotated based on conceptually sound guidelines. While there is a growing number of datasets for the more general phenomenon of hate speech, the development of corpora and annotation guidelines for antisemitic and conspiracy content is still in its infancy, especially for languages other than English. We contribute to closing this gap by developing an annotation guide for antisemitic and conspiracy theory online content in the context of the COVID-19 pandemic. We provide working definitions, including specific forms of antisemitism such as encoded and post-Holocaust antisemitism. We use these to annotate a German-language dataset consisting of ~3,700 Telegram messages sent between 03/2020 and 12/2021.