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In opdracht van het Wetenschappelijk Onderzoek- en Documentatiecentrum (WODC) van het ministerie van Veiligheid en Justitie (VenJ) heeft Coosto een verkennende studie uitgevoerd naar mogelijke nieuwe social media toepassingen voor VenJ. Deze studie beoogt primair nieuwe toepassingen in kaart te brengen of bestaande toepassingen te herformuleren zodat ze bruikbaar zijn voor VenJ. Het daadwerkelijk ontwikkelen / bouwen van nieuwe toepassingen behoort niet tot deze opdracht.

This article examines the forms and feelings of togetherness evident in both Manchester city centre and on social media during the first anniversary of the 22 May 2017 Manchester Arena bombing. To do this, we introduce a conceptual framework that conceives commemorative public atmospheres as composed of a combination of ‘more or less digital’ elements. We also present a methodological approach that combines the computational collection and analysis of Twitter content with short-term team autoethnography. First, the article addresses the concept of public atmospheres before introducing the case study and outlining our methodology. We then analyse the shifting moods of togetherness created by the official programme of commemorative events known as Manchester Together and their digital mediatisation through Twitter. We then explore a grassroots initiative, #LoveMCRBees, and how it relied on the materialisation of social media logics to connect people. Overall, we demonstrate how public atmospheres, as constituted in more and less digital ways, provide a framework for conceptualising commemorative events, and how togetherness is reworked by social media, especially in the context of responses to terrorism.

In the aftermath of the Boston Marathon bombings Bipartisan Policy Center Homeland Security Project co-chair and former Governor Tom Kean, a 9/11 Commission co-chair, talks to BBC World News about the dangers of online radicalisation. Originally published by the BBC on 26 April 2013

VOX-Pol Blog post.

Adolescents are considered especially vulnerable to extremists’ online activities because they are ‘always online’ and because they are still in the process of identity formation. However, so far, we know little about (a) how often adolescents encounter extremist content in different online media and (b) how well they are able to recognize extremist messages. In addition, we do not know (c) how individual-level factors derived from radicalization research and (d) media and civic literacy affect extremist encounters and recognition abilities. We address these questions based on a representative face-to-face survey among German adolescents (n = 1,061) and qualitative interviews using a think-aloud method (n = 68). Results show that a large proportion of adolescents encounter extremist messages frequently, but that many others have trouble even identifying extremist content. In addition, factors known from radicalization research (e.g., deprivation, discrimination, specific attitudes) as well as extremism-related media and civic literacy influence the frequency of extremist encounters and recognition abilities.

Online video sharing platforms such as YouTube contains several videos and users promoting hate and extremism. Due to low barrier to publication and anonymity, YouTube is misused as a platform by some users and communities to post negative videos disseminating hatred against a particular religion, country or person. We formulate the problem of identification of such malicious videos as a search problem and present a focused-crawler based approach consisting of various components performing several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary profiles serving as training data, stopping criterion, node classifier and queue manager. We implement two versions of the focused crawler: best-first search and shark search. We conduct a series of experiments by varying the seed, number of n-grams in the language model based comparer, similarity threshold for the classifier and present the results of the experiments using standard Information Retrieval metrics such as precision, recall and F-measure. The accuracy of the proposed solution on the sample dataset is 69% and 74% for the best-first and shark search respectively. We perform characterization study (by manual and visual inspection) of the anti-India hate and extremism promoting videos retrieved by the focused crawler based on terms present in the title of the videos, YouTube category, average length of videos, content focus and target audience. We present the result of applying Social Network Analysis based measures to extract communities and identify core and influential users.