Conference Agenda

JS_RN21_RN31_03: The Challenge of Measuring Antisemitism
Wednesday, 21/Aug/2019:
4:00pm - 5:30pm

Session Chair: Wolfgang Aschauer, University of Salzburg
Session Chair: Karin Stoegner, University of Vienna
Location: BS.4.06B
Manchester Metropolitan University Building: Business School, Fourth Floor, North Atrium Oxford Road


Challenges of Measuring and Assessing Antisemitism: Phenomena, Perspectives and Methodological Considerations

Kim Robin Stoller

International Institute for Education and Research on Antisemitism (IIBSA) / Free University Berlin, Germany

Quantitative methodology, aiming to measure antisemitism in Europe or in specific European countries, is confronted with a challenge. Beside the lack of many comparative studies, quantitative attempts must define how they want to assess the current situation. Over the last years several quantitative studies and monitoring reports were published, focusing on different perspectives and phenomena. This paper discusses some of the different existing and possible attempts and methodological challenges, focusing on the following aspects:

1. Surveys on antisemitism: Perception of Jews; direct/ indirect communication; potentiality of action in a changed context/ latency; level of obsession; dehumanization/ demonization; justification/ toleration/ advocacy of antisemitic acts.

2. Jewish people's experiences with hate crime, discrimination and antisemitism.

3. Antisemitic incidents, hate crimes and perpetrators: Civil Society reports on antisemitic incidents and police statistics on antisemitic hate crime.

4. Mobilization of antisemitism: demonstrations, lectures etc.

5. Antisemitic Discourse: Antisemitism in Traditional Media and Social Media

The Challenge of Studying Antisemitism and how Survey Experiments Can Help us to Adress it

Heiko Beyer1, Ulf Liebe2

1Heinrich-Heine-Universität Düsseldorf, Germany; 2University of Warwick

Empirical research on antisemitism grapples with the issue of social desirability bias. In many studies using standardized surveys, the measurements are distorted because respondents lie about their attitudes and behavioral intentions. The presentation promotes a new approach to study antisemitism using survey experiments. This methodological toolbox allows for a more subtle measurement of antisemitism and offers various ways to include variables which account for the normative context of antisemitic speech and other types of action. To illustrate the benefits of the approach we present example studies which were conducted in Germany during the last ten years.

New Ways of Scrutinising Overt and Subtle Antisemitism in Hungary

Ildiko Barna, Arpad Knap

Eotvos Lorand University, Faculty of Social Sciences, Hungary

The level of antisemitism in Hungary has always been among the highest in Europe. Representative surveys show that approximately 33 to 40 per cent of the Hungarian population is antisemitic. Although there has been some fluctuation, the level of antisemitism has remained quite stable. Moreover, we found, based on representative surveys among Hungarian Jews, that although the proportion of those having experienced or witnessed antisemitic acts one year prior to the survey decreased massively from 79 to 58 per cent between 1999 and 2017, the perception of antisemitism severely deteriorated. While in 1999, 37 per cent of Jews thought that antisemitism was strong or very strong in Hungary, in 2017 65 per cent said the same. This high discrepancy between experience and perception is due to several factors, being one of them the spread of online hatred. This fact makes the analysis of online sources necessary.

Due to the vast amount of unstructured online textual data, their examination demands new tools, one of them being Natural Language Processing (NLP). NLP is an interdisciplinary field of research in the intersection of computer science, artificial intelligence, as well as linguistics. In our research, we apply NLP on a massive corpus of recent Hungarian news articles, social media content, and online forum comments. NLP makes possible not only the examination of the structure, the main topics, and actors of overt antisemitism but the identification of underlying subjects and specificities of latent antisemitism. In our paper, we present the first results of our research.

Assessing Antisemitism On Twitter

Daniel Armin Miehling1, Gunther Jikeli2,3,4

1OTH-Regensburg; 2Indiana University, United States of America; 3Potsdam University, Germany; 4CNRS, France

Recent reports on online antisemitism highlight the rise of antisemitism on social media platforms. While this is plausible, several methodological questions arise when assessing such claims. How is the rise of antisemitism measured? How are they quantified in rapidly growing and diversifying platforms? Are the numbers of antisemitic messages rising proportionally to other content or is the share of antisemitic content increasing? Are antisemitic messages mostly disseminated on infamous websites and fora such as The Daily Stormer, 4Chan/pol or 8Chan/pol, Gab, and closed social media groups, or is this a wider phenomenon? However, at the root of methodological questions is the challenge of identifying a consistent way to identify diverse manifestations of antisemitism.

Answering any substantive questions requires reliable methods for identifying antisemitic content in large datasets. Previous studies have used antisemitic keywords and combination of keywords, such as “kike” or “Jews” and “kill” or tracked antisemitic memes. (Finkelstein et al. 2018; Gitari et al. 2015) Others rely on manual classification, but fail to share their classification scheme and do not use representative samples.

This paper presents the methods of a research project that a) aims to provide meaningful figures of the percentage of antisemitic tweets when keywords, such as “Jew*” or “Israel*” are used and b) develops a ground truth dataset of antisemitic/non-antisemitic tweets that can be used in AI programs to find antisemitic tweets in large databases. We also present our approach of using a definition of antisemitism that is transparent and verifiable.