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Combating ISIS Online Panelists talked about *ISIS’ use of the Internet and social media to recruit supporters. They also addressed the U.S. government’s efforts to combat the terror group online. Other topics included whether internet and social media companies should be forced to identify and/or ban those suspected of promoting terror.

The Islamic State of Iraq and Syria (ISIS), the Islamic State of Iraq and the Levant (ISIL), or DAISH/DAESH in Arabic is a militant group that has called itself the Islamic State.

Financiers and fundraisers for al-Qaida and Islamic State (ISIS) are active
users of popular social media platforms, such as Facebook, Twitter,
YouTube and Instagram, in some cases even after being placed on a
United Nations or US government sanctions list. Terrorist financiers
and fundraisers have utilized social media to attract and direct funding
to procure weapons, pay salaries, strengthen infrastructure and operate
civil and social services. While the amount of funding raised via social
media is far less in comparison to revenues from oil sales or taxation,
al-Qaida and ISIS fundraisers have taken credit for millions of dollars
raised using social media-based campaigns—significant amounts by
any standard.
Terrorist financiers and fundraisers for al-Qaida and ISIS have relied on
social media services to communicate with colleagues and supporters,
attract new followers globally, and promote aligned causes and
organizations. With their potential to spur viral content growth, social
media services enable fundraisers to more quickly and effectively solicit
support and reach larger audiences.
Social media companies have actively terminated the accounts of
terrorist facilitators—including a number of designated terrorist
fundraisers and financiers—citing violations of their respective terms
of service restrictions that prohibit support for violence or hate speech
[see pg.12]. For example, in early February 2016 Twitter announced the
closure of more than 125,000 accounts “for threatening or promoting
terrorist acts, primarily related to ISIS,” noting that social media platforms
are “forced to make challenging judgment calls based on very limited
information and guidance.”1 Facebook has also stepped up its efforts
to remove users who back terror groups, and YouTube has taken down
content and terminated users who post terrorist material.2

This article seeks to investigate the way in which social media can affect terrorist events. Using the 2014 Sydney siege as its primary focus, it will argue that the public’s social media activity, particularly the capacity to engage in ‘reporting’ of live events as they occur, can shift the tactical advantage from counterterrorism officials to the perpetrator. Situational awareness theory will be used to analyse how the public’s Twitter activity during the event had the capacity to enhance the perpetrator’s decision-making and therefore his overall capacity to execute the attack. The article will analyse the Martin Place Siege Joint Commonwealth—New South Wales Review, particularly, Chapter 10, Public Communication. The Review had shortcomings in terms of its failure to fully analyse the role of social media during the Sydney siege and the way in which it impacted upon events. The article therefore seeks to highlight the need for law enforcement and government agencies to take into account developments within social media, which have added a new dimension to terrorist activity. Failure to take account of these developments will diminish the capacity of law enforcement and government to respond effectively to similar events in the future.

The main purpose of this policy brief is to highlight the risks of radicalization by utilizing statistical analysis. The main question interrogated here is to determine the relation between the level of social reaction and ISIS’s attacks. These reactions are measured through Google Trends of news, web searches, and video ratings. Methodologically, Turkey, France, Belgium, the United Kingdom and Germany are picked as the sample countries. In order to derive consistent data from Google’s database, the keywords “Islamic State of Iraq and the Levant,” and its some acronyms such as ISIS, ISIL, Islamic State, and DAESH are utilized.

This analysis revealed that Internet media might function as a recognition tool for terrorist organizations considering the fact that following ISIS’s attacks, people of both the target country and other countries progressively search ISIS on Google. Thus, one way or another, ISIS gains reputation and takes the opportunity for radicalization. Furthermore, the level of these searches differs among the aforementioned countries due to specific reasons, which are elaborated in this policy brief.

Advances in digital technologies have provided ample positive impacts to modern society; however, in addition to such benefits, these innovations have inadvertently created a new venue for criminal activity to generate.

Combating Violent Extremism and Radicalization in the Digital Era is an essential reference for the latest research on the utilization of online tools by terrorist organizations to communicate with and recruit potential extremists and examines effective countermeasures employed by law enforcement agencies to defend against such threats. Focusing on perspectives from the social and behavioral sciences, this book is a critical source for researchers, analysts, intelligence officers, and policy makers interested in preventive methods for online terrorist activities.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.