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The Digital Services Act (DSA) requires designated Very Large Online Platforms and Very Large Online Search Engines (VLOPs and VLOSEs) conduct assessments of the systemic risk of the dissemination of illegal content through their services- and to mitigate such risk where it arises. With regulators, researchers, civil society, and the general public finally due to see the public versions of the first systemic risk assessments conducted by VLOP and VLOSE providers, robust evaluation of the DSA’s systemic risk management approach to the dissemination of illegal content is crucial.  

CERRE’s Benchmarks for Evaluating Management of Risk of Terrorist Content Dissemination issue paper contributes to this evaluation by examining the systemic risk management approach to the dissemination terrorist content. Terrorist content is a specific subset of illegal content under the DSA that is also the subject of a complex nexus of other EU legislation, including the TERREG and several transnational initiatives. The determination of terrorist content poses a significant challenge, as authorities and platforms grapple with the use of sophisticated evasive techniques by terrorist actors, including the use of coded content or “beacons”, while also seeking to respect the fundamental rights of platform users.  

In this issue paper, author  Sally Broughton Micova explores the key definitional issues surrounding terrorist content and maps the ecosystem of actors involved in its risk assessment, mitigation, enforcement, and dissemination. Based on detailed analysis, the paper develops a series of benchmarks and concomitant metrics for the systemic risk evaluation of terrorist content. 

The purpose of this study is to synthesize the literature relating to radicalization on social media, a space with enhanced concerns about nurturing propaganda and conspiracies for violent extremism. Through the systematic review of 82 peer-reviewed studies related to radicalization through social media published in scholarly journals, this paper evidence the growth of robust studies on the usage of social media for radicalization. Nonetheless, the current work hardly discusses the radicalization issues through social media and reveals an increasing trend of publication from 2017 with a major contribution from the USA, Germany, and England. The thematic analysis indicated determinants of radicalization and the mitigation measures for the deradicalization of content on social media. However, the knowledge gap persists to understand the effects of radicalization in the different regional settings and further framing of content specific to target populations. Individuals must have the critical social media literacy to counteract the rising radicalization through social media. Individual users’ political interests are key factors in their radicalization such as citizens losing faith in the government and political parties. Active rather than passive searchers of violent radical material are more likely to engage in political violence. The results indicate that further research using experimental design, grounded theory, and pilot interventions may be relevant to suggest a solution to mitigate radicalization on social media.

The article describes a systematic approach used by the Swiss Intelligence Service (SAP). The procedure described is illustrated by the authentic case of messages among extremists going on the Website www.islamic-minbar.com, a case that was prosecuted and brought to verdict by the Attorney General of the Swiss Confederation.

Over the last four years as the Syrian uprising has grown into a full-blown civil war, a sinister parallel conflict has been fought out in cyberspace, with combatants wielding bytes and software rather than guns as they have battled for supremacy on Syria’s internet frontline.

But the consequences of this secret cyber war have been real and deadly – particularly for opponents of the Assad regime who have been targeted for arrest and torture as a consequence of personal information gleaned from their email traffic.

In some cases even the military plans of crucial rebel offensives had been hacked. But the opposition has been busy too, leaking President Bashar al-Assad’s embarrassing personal correspondence and eavesdropping on government troop deployments amid much else.

As a consequence Syria’s civil war has become fertile ground for ‘hacktivists’ from both sides – egged on and in some cases assisted by governments and agencies from outside the region.

In this special investigation for People & Power , Juliana Ruhfus has been finding out why some experts believe Syria’s electronic armies have been drawing up the blueprints for all wars of the future, conflicts that transcend traditional physical boundaries but which can be just as significant as those fought with tanks and missiles.

Social media platforms have recently seen an increase in the occurrence of hate speech discourse which has led to calls for improved detection methods. Most of these rely on annotated data, keywords, and a classification technique. While this approach provides good coverage, it can fall short when dealing with new terms produced by online extremist communities which act as original sources of words which have alternate hate speech meanings. These code words (which can be both created and adopted words) are designed to evade automatic detection and often have benign meanings in regular discourse. As an example, “skypes”, “googles”, and “yahoos” are all instances of words which have an alternate meaning that can be used for hate speech. This overlap introduces additional challenges when relying on keywords for both the collection of data that is specific to hate speech, and downstream classification. In this work, we develop a community detection approach for finding extremist hate speech communities and collecting data from their members. We also develop a word embedding model that learns the alternate hate speech meaning of words and demonstrate the candidacy of our code words with several annotation experiments, designed to determine if it is possible to recognize a word as being used for hate speech without knowing its alternate meaning. We report an inter-annotator agreement rate of K=0.871, and K=0.676 for data drawn from our extremist community and the keyword approach respectively, supporting our claim that hate speech detection is a contextual task and does not depend on a fixed list of keywords. Our goal is to advance the domain by providing a high quality hate speech dataset in addition to learned code words that can be fed into existing classification approaches, thus improving the accuracy of automated detection.

VOX-Pol Blog Post.