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Online antisemitism is hard to quantify. How can it be measured in rapidlygrowing and diversifying platforms? Are the numbers of antisemitic messagesrising proportionally to other content or is it the case that the share ofantisemitic content is increasing? How does such content travel and what arereactions to it? How widespread is online Jew-hatred beyond infamous websitesand fora, and closed social media groups? However, at the root of manymethodological questions is the challenge of finding a consistent way toidentify diverse manifestations of antisemitism in large datasets. What ismore, a clear definition is essential for building an annotated corpus that canbe used as a gold standard for machine learning programs to detect antisemiticonline content. We argue that antisemitic content has distinct features thatare not captured adequately in generic approaches of annotation, such as hatespeech, abusive language, or toxic language. We discuss our experiences withannotating samples from our dataset that draw on a ten percent random sample ofpublic tweets from Twitter. We show that the widely used definition ofantisemitism by the International Holocaust Remembrance Alliance can be appliedsuccessfully to online messages if inferences are spelled out in detail and ifthe focus is not on intent of the disseminator but on the message in itscontext. However, annotators have to be highly trained and knowledgeable aboutcurrent events to understand each tweet's underlying message within itscontext. The tentative results of the annotation of two of our small butrandomly chosen samples suggest that more than ten percent of conversations onTwitter about Jews and Israel are antisemitic or probably antisemitic. Theyalso show that at least in conversations about Jews, an equally high number oftweets denounce antisemitism, although these conversations do not necessarilycoincide.

Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism