  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.
