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Lately, the Islamic State of Iraq and Syria (ISIS) has managed to control large parts of Syria and Iraq. To better understand the roots of support for ISIS, we present a study using Twitter data. We collected a large number of Arabic tweets referring to ISIS and classified them as pro-ISIS or anti-ISIS. We then analyzed the historical timelines of both user groups and looked at their pre-ISIS period to gain insights into the antecedents of support. Also, we built a classifier to ‘predict’, in retrospect, who will support or oppose the group. We show that ISIS supporters largely differ from ISIS opposition in that the former referred a lot more to Arab Spring uprisings that failed than the latter.

The Nigeria-based violent non-state actor Boko Haram is increasingly reported on in the news media in relation to the Islamic State, another, more prominent, violent non-state actor. In particular, these comparisons have been drawn within the context of reports on Boko Haram’s recent improvement in video propaganda quality. While the associations with the Islamic State are often warranted, there are broader social consequences when colonial power relations are brought into play. Borrowing an approach from critical discourse analysis, 16 online English-language news articles were read through a postcolonial lens in order to analyse the structural relations of dominance that arise when discussing African non-state actors. The analysis revealed that among the corpus of articles, nine developed a discourse of mimicry, which serves to deny Boko Haram full agency, relegate them to a silenced subaltern status, and ultimately to diminish the sense of threat posed to the dominant geopolitical security paradigm.

Amid mounting death tolls in Iraq, Syria, and Yemen, sectarian discourse is
on the rise across the Arab world—particularly in the online sphere, where
extremist voices are amplified and violent imagery and rhetoric spreads rapidly.
Despite this, social media also provides a space for cross-sectarian discourse
and activism. Analysis of over 7 million Arabic tweets from February to August
2015 suggests that violent events and social network structures play key roles
in the transmission of this sectarian and countersectarian rhetoric on Twitter.

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 channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.