Online Library

Rassistische Hassrede im Internet: Wie soll die Schweiz in Zukunft damit umgehen? Der vorliegende Bericht, welchen die Fachstelle für Rassismusbekämpfung (FRB) in Auftrag gegeben hat, liefert dazu einen Beitrag. Er beinhaltet eine kritische Zusammenfassung der Datenlage, einen Einblick in die digitale Umwelt und egünstigenden Kommunikationsbedingungen rassistischer Online-Hassrede sowie einen Überblick zu bestehenden Gegenmassnahmen in der Schweiz und im Ausland. Schliesslich werden die Herausforderungen und Anliegen von Schweizer Verwaltungsstellen, Beratungsstellen und privaten Organisationen identifiziert und Handlungsempfehlungen für die StahelSchweiz abgegeben.

Steganography is the art and science of
hiding the fact that communication is taking place by
hiding information in other information’ (Johnson).
According to nameless “U.S. officials and experts”
and “U.S. and foreign officials,” terrorist groups
are “hiding maps and photographs of terrorist
targets and posting instructions for terrorist
activities on sports chat rooms, pornographic
bulletin boards and other Web sites”. This paper
informs the reader how an innocent looking digital
image hides a deadly terrorist plan, which can
destroy the world just in a click of a mouse. It also
describes and discusses the process of secret
communication known as steganography. The
crucial argument here is that terrorists are most
likely to be employing digital steganography to
facilitate secret intra-group communication as has
been claimed. This is mainly because the use of
digital steganography by terrorist is both technically
and operationally dubious. The most important part
this paper discusses is that terrorist are likely to
employ low-tech steganography such as semagrams
and null chippers instead. It investigates the
strengths of image steganography and the reasons
why terrorists are relying on it so much.

VOX-Pol Blog post.

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~ 96k and a Twitter dataset of size ~ 24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.

This report describes how terrorist, violent extremist and organized criminal groups are trying to take advantage of the Coronavirus disease (COVID-19) pandemic to expand their activities and jeopardize the efficacy and credibility of response measures by governments.

Although the subject of extreme right virtual community formation is often discussed, an online ‘sense of community’ among right-wing extremists has not been systematically analysed. It is argued that to study this phenomenon and to understand its backgrounds and function, the offline and online experiences and actions of those involved need to be taken into account. For this purpose, qualitative data has been collected on the web forum ‘Stormfront’, supplemented by extensive online interviews with eleven of its members. It is demonstrated that those experiencing stigmatization in offline social life regard the forum as a virtual community that functions as an online refuge, whereas those who – because of special circumstances – do not experience offline stigmatization do not display an online sense of community. It is concluded that offline stigmatization underlies virtual community formation by Dutch right-wing extremists. Because this mechanism may have broader significance, additional hypotheses for future research are formulated.