A global network of anti-Muslim activists is using Twitter bots, fake news and the manipulation of images to influence political discourse, new analysis reveals. Many have recorded significant growth in their social media followings over the past year, co-ordinating to push the message that Islam is an “imminent threat” to western society.
Researchers from the anti-racist organisation Hope Not Hate found that the impact of tweets from one controversial US activist, Pamela Geller, who is banned from the UK, is magnified by 102 bots, automated or semi-automated accounts that automatically tweet or retweet their content. >Researchers also monitored a sample of popular anti-Muslim Twitter accounts in Britain and the US between March and November this year, and found that, on average, there was a 117% growth in followers.
Geller, described by critics as a figurehead for Islamophobic organisations,produces the Geller Report, which doubled its viewers to more than two million people each month between July and October. The Gates of Vienna counter-jihadist blog, described by critics as a training manual for anti-Muslim paramilitaries, also doubled in visitors per month during the same period.
Patrik Hermansson, researcher for Hope not Hate, said: “The growth among Twitter accounts and websites spreading anti-Muslim hate is alarming. In such a key area of public interest, it is an indication of increased interest in these views and, as each account or site grows, more people are exposed to deeply prejudiced anti-Muslim views.”
The study also charts how terror attacks in the UK have been exploited by anti-Muslim activists over social media, with several prominent anti-Muslim Twitter accounts in the UK acquiring a significant number of followers in their aftermath.
During the hours and days following the Manchester attack, Tommy Robinson, former leader of the English Defence League (EDL), gained 40,042 followers, an increase of 17%, with the majority – 29,396 – coming within 48 hours of the attack. Robinson gained 22,365 after the Westminster attack: he had a weekly average increase of 6,422 followers from March to November 2017.
The aftermath of the London Bridge attack in June was used to illustrate how anti-Muslim activists took advantage, with 32 of the top 100 most shared tweets about the attack expressing negative sentiments about Muslims.
The study also accuses Breitbart, run by Donald Trump’s former chief strategist Steve Bannon, of spreading fake news, stating that “its reporting on Islam and Muslims is largely indistinguishable from the anti-Muslim movement’s rhetoric or even that of the far right”.
The study says a network of online forums and image boards serves as an echo chamber to amplify and spread fabricated anti-Muslim social media campaigns. The most notorious recent example was the exploitation of a photograph of a Muslim woman walking past a group of people helping a victim of the Westminster attack in March 2017.
The image gained traction after a Twitter user called @Southlonestar claimed the image revealed the woman’s indifference to the victim being treated. It was recently revealed that@Southlonestar was one of 2,700 accounts handed over to the US House Intelligence Committee by Twitter as a fake account created in Russia to influence UK and US politics.
The image of the Muslim woman – who has since spoken of her distress at the attack and the abuse she suffered afterwards – was later superimposed on pictures after the Manchester attack.
Researchers claim in their report that bots were employed to amplify Geller’s messages on Twitter, identifying at least 102 accounts that exhibit characteristics of bots, including only exclusively posting content with links to Geller’s website and being highly synchronised, meaning they post the same content at almost the same time.
The simplest bots follow and retweet other users. A user with a large number of followers is generally easier to trust and may seem more “legitimate”. The more advanced bots often mix human control with artificial intelligence, and are notoriously difficult to detect.
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