Online Library

This article critically explores the historical representation of the Middle East in video games until 2002 and following the 9/11 event. Using thematic analysis, we identify four main themes and discuss how this representation shifts with time, offering a brief overview of the broader context. I argue that there is a clear association between political developments and this western representation, for we can clearly observe changes from the romantic, exotic, and orientalist views of the region and Arab and Muslim figure shown within the realms of the Arabian Nights theme to a more militant figure and dangerous place due to featuring armed conflict in the Middle East such as the first Gulf War. Though not included in the discussion, 9/11 introduced a more negative and Islamophobic representation that is associated with Islamist terrorism, and it marks the latest development in these themes.

This book offers empirical insight into the way Muslims reacted online towards various controversial issues related to Islam. The book examines four cases studies: The Muhammed’s cartoons, the burning of the Quran controversies, Fitna and the Innocence of Muslims’ films. The issues of online religion, social movements and extremism are discussed, as many of the cases in question created both uproar and unity among many YouTubers. These case studies – in some instances – led to the expression of extremist views by some users, and the volume argues that they helped contribute to the growth of extremism due to the utilization of these events by some terrorist groups in order to recruit new members. In the concluding chapter, social network and sentiment analyses are presented in order to investigate all the collected comments and videos, while a critical discussion of freedom of expression and hate speech is offered, with special regards to the growing online influence of far right groups and their role in on-going YouTube debates.

The anti-terror public media campaigns started in Iraq around 2004 and was called ‘Terror has no Religion’ in order to combat the threats of sectarianism and Al-Qaeda. After the withdrawal of the US forces from the country in late 2010, the campaign stopped, but a new and similar one emerged that is called ‘Say no to Terror’ whose advertisements mostly targeted the Saudi public. Several Pan-Arab regional channels like Al-Arabiya and Middle East Broadcasting Center (MBC) were part of airing its advertisements. This study focuses on the ‘Terror has no Religion’ and ‘Say no to Terror’ campaigns by critically examining their websites and videos to understand the nature of messages sent to the public. Further, the study examines the effectiveness of the two campaigns with special emphasis on ‘Say no to Terror’ by analyzing comments posted on YouTube and discussing the counter campaign. Over 350 videos were found containing counter arguments to ‘Say no to Terror’ campaign, and about 60% of YouTube commentators viewed the campaign negatively, expressing suspicion about its real intentions. The paper concludes that the success of such public service advertisements is doubtful due to the format of the message as well as cultural and political reasons that are linked to the region.

What characterises social media radicals? And why some people become attracted to radicalisation? To explore answers to these questions, a number of tweets posted by a group of suspected radicals tweeting in Arabic were analysed using social network analysis and machine learning. The study revealed that these suspected radicals’ networks showed significant interaction with others; but this interactivity is only significant quantitatively as the interaction is not reciprocated. With regards to why these suspected radicals became attracted to radicalisation, Topic Modelling revealed these suspected radicals’ tweets underpinned a perceived injustice that they believed the Secret Police and the Government inflicted upon them. Overall, the study has shown that data science tools have the potential to inform our understanding of online radicalisation. It is hoped this exploratory study will be the basis for a future study in which the research questions will be answered using a larger sample.

The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

VOX-Pol Blog Post.