Online Library

Young People Combating Hate Speech Online is a project of the Council of Europe’s youth sector running between 2012 and 2015. The project aims to combat racism and discrimination in their online expression of hate speech by equipping young people and youth organisations with the competences necessary to recognize and act against such human rights violations. Central to the project is a European youth media campaign which will be designed and implemented with the agency of young people and youth organisations. As a preparation for the project, the Council of Europe’s Youth Department commissioned three “mapping” studies about the realities of hate speech and young people and projects and campaigns about it. These studies are published here as a resource for the activists, youth leaders, researchers, partners and decision makers associated to the project and the online campaign. They are truly a starting points: more research is needed, both on the legal and policy implications of hate speech online as on its impact and relation with young people.

Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to assess the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered.

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.