Toxic Online Discussions during the UK European Parliament Election Campaign

The Brexit Party attracted the most engagement on Twitter in the run-up to the UK European Parliament election on May 23rd, their candidates receiving as many tweets as all the other parties combined. Brexit Party leader Nigel Farage was the most interacted-with UK candidate on Twitter, with over twice as many replies as the next most replied-to candidate, Andrew Adonis of the Labour Party.

We studied all tweets sent to or from (or retweets of or by) UK European Election candidates in the month of May, and classified them as abusive or not using the classifier presented here. It must be noted, in particular, that the classifier only identifies reliably whether a reply is abusive or not. It is not sufficiently accurate for us to reliably judge the target politician or party of this abusive reply. What this means is that we can only reliably identify which EP candidates triggered abuse-containing discussion threads on Twitter, but that often this abuse is actually aimed at other politicians or parties.

In addition to attracting the most replies, the Brexit Party candidates also triggered an unusually high level of abuse-containing Twitter discussions. In particular, we found that posts by Farage triggered almost six times as many abuse-containing Twitter threads than the next most replied to candidate, Gavin Esler of Change UK, during May 2019.

There is an important difference, however, in that that many of the abuse-containing replies to posts by Farage and the Brexit Party were actually abusive towards other politicians (most notably the prime minister and the leader of the Labour party) and not Farage himself. In contrast, abusive replies to Gavin Esler were primarily aimed at the politician himself, triggered by his use of the phrase "village idiot" in connection with the Leave Campaign.

Candidates from other parties that triggered unusually high levels of abuse-containing discussions were those from the UK Independence Party, now considered far right, and Change UK, a newly formed but unstable remain party. Change UK was the most active on Twitter, with candidates sending more tweets than other parties. Gavin Esler was the most replied-to Change UK candidate, and also received an unusually high level of abuse. The abuse often referred to his use of the phrase "village idiot" in connection with the leave campaign, which resulted in anger and resentment.

In contrast, MEP candidates from the Conservative and Labour Parties were not hubs of polarised, abuse-containing discussions on Twitter.

What these findings, unsurprisingly, demonstrate is that politicians and parties who themselves use divisive and abusive language, for example, to brand political opponents as “village idiots”, “traitors”, or as “desperate to betray”, are thus triggering the toxic online responses and deep political antagonism that we have witnessed.

After the Brexit Party, the next most replied-to MEP candidates were from the Labour partyAfter the Brexit Party, the next most replied-to party was Labour, according to the study, followed by Change UK.

MEP candidates from both the Liberal Democrats and the Green Party were also active on Twitter, with the Green MEP candidates second only to Change UK ones for number of tweets sent, but didn't get a lot of engagement in return. The Liberal Democrats in particular received a low number of replies. This may suggest that these parties became the choices of default for a population of discouraged remainers, as both made gains in the election. Both parties attracted a particularly civil tone of reply.

Brexit Party candidates were also the ones that replied most to those who tweeted them, rather than authoring original tweets or retweeting other tweets.

Acknowledgements: Research carried out by Genevieve Gorrell, Mehmet Bakir, and Kalina Bontcheva. This work was partially supported by the European Union under grant agreements No. 654024 SoBigData and No. 825297 WeVerify.

WeVerify: Algorithm-Supported Verification of Digital Content

Announcing WeVerify: a new project developing AI-based tools for computer-supported digital content verification. The WeVerify platform will provide an independent and community driven environment for the verification of online content, to be used to assist journalists in gathering and verifying quickly online content. Prof. Kalina Bontcheva will be serving as the Scientific Director of the project.

Online disinformation and fake media content have emerged as a serious threat to democracy, economy and society. Content verification is currently far from trivial, even for experienced journalists, human rights activists or media literacy scholars. Moreover, recent advances in artificial intelligence (deep learning) have enabled the creation of intelligent bots and highly realistic synthetic multimedia content. Consequently, it is extremely challenging for citizens and journalists to assess the credibility of online content, and to navigate the highly complex online information landscapes.

WeVerify aims to address the complex content verification challenges through a participatory verification approach, open source algorithms, low-overhead human-in-the-loop machine learning and intuitive visualizations. Social media and web content will be analysed and contextualised within the broader online ecosystem, in order to expose fabricated content, through cross-modal content verification, social network analysis, micro-targeted debunking and a blockchain-based public database of known fakes.

Add caption

A key outcome will be the WeVerify platform for collaborative, decentralised content verification, tracking, and debunking.

The platform will be open source to engage communities and citizen journalists alongside newsroom and freelance journalists. To enable low-overhead integration with in-house content management systems and support more advanced newsroom needs, a premium version of the platform will also be offered. It will be furthermore supplemented by a digital companion to assist with verification tasks.

Results will be validated by professional journalists and debunking specialists from project partners (DW, AFP, DisinfoLab), external participants (e.g. members of the First Draft News network), the community of more than 2,700 users of the InVID verification plugin, and by media literacy, human rights and emergency response organisations.

The WeVerify website can be found at https://weverify.eu/, and WeVerify can be found on Twitter @WeV3rify!

Brexit--The Regional Divide

Referendum result

Although the UK voted by a narrow margin in the UK EU membership referendum in 2016 to leave the EU, that outcome failed to capture the diverse feelings held in various regions. It's a curious observation that the UK regions with the most economic dependence on the EU were the regions more likely to vote to leave it. The image below on the right is taken from this article from the Centre for European Reform, and makes the point in a few different ways. This and similar research inspired a current project the GATE team are undertaking with colleagues in the Geography and Journalism departments at Sheffield University, under the leadership of Miguel Kanai and with funding from the British Academy, aiming to understand whether lack of awareness of individual local situation played a role in the referendum outcome.

Our Brexit tweet corpus contains tweets collected during the run-up to the Brexit referendum, and we've annotated almost half a million accounts for Brexit vote intent with a high accuracy. You can read about that here. So we thought we'd be well positioned to bring some insights. We also annotated user accounts with location: many Twitter users volunteer that information, though there can be a lot of variation on how people describe their location, so that was harder to do accurately. We also used local and national news media corpora from the time of the referendum, in order to contrast national coverage with local issues are around the country.

Topics representation in different media

"People's resistance to propaganda and media‐promoted ideas derives from their close ties in real communities" Jean Seaton

Using topic modelling and named entity recognition, we were able to look for similarities and differences in the focus of local and national media and Twitter users. The bar chart on the left gets us started, illustrating that foci differ between media. Twitter users give more air time than news media to trade and immigration, whereas local press takes the lead on employment, local politics and agriculture. National press gives more space to terrorism than either Twitter or local news.

NER diff between national and local press

On the right is just one of many graphs in which we unpack this on a region-by-region basis (you can find more on the project website). In this choropleth, red indicates that the topic was significantly more discussed in national press than in local press in that area, and green indicates that the topic was significantly more discussed in local press there than in national press. Terrorism and immigration have perhaps been subject to a certain degree of media and propaganda inflation--we talk about this in our Social Informatics paper. Where media focus on locally relevant issues, foci are more grounded, for example in practical topics such as agriculture and employment. We found that across the regions, Twitter remainers showed a closer congruence with local press than Twitter leavers.

The graph on the right shows the number of times a newspaper was linked on Twitter, contrasted against the percentage of people that said they read that newspaper in the British Election Study. It shows that the dynamics of popularity on Twitter are very different to traditional readership. This highlights a need to understand how the online environment is affecting the news reportage we are exposed to, creating a market for a different kind of material, and a potentially more hostile climate for quality journalism, as discussed by project advisor Prof. Jackie Harrison here. Furthermore, local press are increasingly struggling to survive, so it feels important to highlight their value through this work.
You can see more choropleths on the project website. There's also an extended version here of an article currently under review.

Russian Troll Factory: Sketches of a Propaganda Campaign

When Twitter shared a large archive of propaganda tweets late in 2018 we were excited to get access to over 9 million tweets from almost 4 thousand unique Twitter accounts controlled by Russia's Internet Research Agency. The tweets are posted in 57 different languages, but most are in Russian (53.68%) and English (36.08%). Average account age is around four years, and the longest accounts are as much as ten years old.
A large amount of activity in both the English and Russian accounts is given to news provision. Secondly, many accounts seem to engage in hashtag games, which may be a way to establish an account and get some followers. Of particular interest however are the political trolls. Left trolls pose as individuals interested in the Black Lives Matter campaign. Right trolls are patriotic, anti-immigration Trump supporters. Among left and right trolls, several have achieved large follower numbers and even a degree of fame. Finally there are fearmonger trolls, that propagate scares, and a small number of commercial trolls. The Russian language accounts also divide on similar lines, perhaps posing as individuals with opinions about Ukraine or western politics. These categories were proposed by Darren Linvill and Patrick Warren, from Clemson University. In the word clouds below you can see the hashtags we found left and right trolls using.

Left Troll Hashtags

Right Troll Hashtags

Mehmet E. Bakir has created some interactive graphs enabling us to explore the data. In the network diagram at the start of the post you can see the network of mention/retweet/reply/quote counts we created from the highly followed accounts in the set. You can click through to an interactive version, where you can zoom in and explore different troll types.
In the graph below, you can see activity in different languages over time (interactive version here, or interact with the embedded version below; you may have to scroll right). It shows that the Russian language operation came first, with English language operations following after. The timing of this part of the activity coincides with Russia's interest in Ukraine.

In the graph below, also available here, you can see how different types of behavioural strategy pay off in terms of achieving higher numbers of retweets. Using Linvill and Warren's manually annotated data, Mehmet built a classifier that enabled us to classify all the accounts in the dataset. It is evident that the political trolls have by far the greatest impact in terms of retweets achieved, with left trolls being the most successful. Russia's interest in the Black Lives Matter campaign perhaps suggests that the first challenge for agents is to win a following, and that exploiting divisions in society is an effective way to do that. How that following is then used to influence minds is a separate question. You can see a pre-print of our paper describing our work so far, in the context of the broader picture of partisanship, propaganda and post-truth politics, here.

Funded PhD Opportunity: Large Scale Analysis of Online Disinformation in Political Debates

Applications are invited for an EPSRC-funded studentship at The University of Sheffield commencing on 1 October 2018.

The PhD project will examine the intersection of online political debates and misinformation, through big data analysis. This research is very timely, because online mis- and disinformation is reinforcing the formation of polarised partisan camps, sharing biased, self-reinforcing content. This is coupled with the rise in post-truth politics, where key arguments are repeated continuously, even when proven untrue by journalists or independent experts. Journalists and media have tried to counter this through fact-checking initiatives, but these are currently mostly manual, and thus not scalable to big data.

The aim is to develop machine learning-based methods for large-scale analysis of online misinformation and its role in political debates on online social platforms.



Application deadline: as soon as possible, until the funding is filled  
Interviews: interviews take place within 2-3 weeks of application

Supervisory team: Professor Kalina Bontcheva (Department of Computer Science, University of Sheffield), Professor Piers Robinson (Department of Journalism, University of Sheffield), and Dr. Nikolaos Aletras (Information School, University of Sheffield).

Award Details

The studentship will cover tuition fees at the EU/UK rate and provide an annual maintenance stipend at standard Research Council rates (£14,777 in 2018/19) for 3.5 years.

Eligibility

The general eligibility requirements are:

• Applicants should normally have studied in a relevant field to a very good standard at MSc level or equivalent experience.
• Applicants should also have a 2.1 in a BSc degree, or equivalent qualification, in a related discipline.
• ESRPC studentships are only available to students from the UK or European Union. Applications cannot be accepted from students liable to pay fees at the Overseas rate. Normally UK students will be eligible for a full award which pays fees and a maintenance grant if they meet the residency criteria and EU students will be eligible for a fees-only award, unless they have been resident in the UK for 3 years immediately prior to taking up the award.
  

How to apply

To apply for the studentship, applicants need to apply directly to the University of Sheffield for entrance into the doctoral programme in Computer Science 

• Complete an application for admission to the standard computer science PhD programme http://www.sheffield.ac.uk/postgraduate/research/apply 
• Applications should include a research proposal; CV; academic writing sample; transcripts and two references.
• The research proposal of up to 1,000 words should outline your reasons for applying to this project and how you would approach the research including details of your skills and experience in both computing and/or data journalism.
• Supporting documents should be uploaded to your application.

Discerning Truth in the Age of Ubiquitous Disinformation (5): Impact of Russia-linked Misinformation vs Impact of False Claims Made By Politicians During the Referendum Campaign

Discerning Truth in the Age of Ubiquitous Disinformation (5)

Impact of Russia-linked Misinformation vs Impact of False Claims Made By Politicians During the Referendum Campaign

Kalina Bontcheva (@kbontcheva)


My previous post focuses mainly on the impact of misinformation from Russian Twitter accounts.  However it is important to also acknowledge the impact of false claims made by politicians which were shared and distributed through social media.

A House of Commons Treasury Committee Report published on May 2016, states that: “The public debate is being poorly served by inconsistent, unqualified and, in some cases, misleading claims and counter-claims. Members of both the ‘leave’ and ‘remain’ camps are making such claims. Another aim of this report is to assess the accuracy of some of these claims..”

In our research, we analysed the number of Twitter posts around some of the these disputed claims, firstly to understand their resonance with voters, and secondly, to compare this to the volume of Russia-related tweets discussed above.

A study  of the news coverage of the EU Referendum campaign established that the economy was the most covered issue, and in particular, the Remain claim that Brexit would cost households £4,300 per year by 2030 and the Leave campaign’s claim that the EU cost the UK £350 million each week. Therefore, we focused on  these two key claims and analysed tweets about them.

With respect to the disputed £4,300 claim (made by the Chancellor of the Exchequer), we  identified 2,404 posts in our dataset (tweets, retweets, replies), referring to this claim.

For the £350 million a week disputed claim - there are 32,755 pre-referendum posts (tweets, retweets, replies) in our dataset. This is 4.6 times the 7,103 posts related to Russia Today and Sputnik and 10.2 times more than the 3,200 tweets by the Russia-linked accounts suspended by Twitter.

In particular, there are more than 1,500 tweets from different voters, with one of these wordings:

I am with @Vote_leave because we should stop sending £350 million per week to Brussels, and spend our money on our NHS instead.

I just voted to leave the EU by postal vote! Stop sending our tax money to Europe, spend it on the NHS instead! #VoteLeave #EUreferendum

Many of those tweets have themselves received over a hundred likes and retweets each.

This false claim is being regarded by media as one of the key ones behind the success of VoteLeave.

So returning to Q27 on likely impact of misinformation on voting behaviour - it was not possible for us to quantify this from such tweets alone. A potentially useful indicator comes from an Ipsos Mori poll published on 22 Jun 2016, which  showed that for 9% of respondents the NHS was the most important issue in the campaign.


In conclusion, while it is important to quantify the potential impact of Russian misinformation, we should also consider the much wider range of misinformation that was posted on Twitter and Facebook during the referendum and its likely overall impact.

We should also study not just fake news sites and the social platforms that were used to disseminate misinformation, but also the role and impact of Facebook-based algorithms for micro-targeting adverts, that have been developed by private third parties.

A related question, is studying the role played by hyperpartisan and mainstream media sites during the referendum campaign. This is the subject of our latest study, with key findings available here
.
High Automation Accounts in Our Brexit Tweet Dataset

While it is hard to quantify all different kinds of fake accounts, we know already that a study by City University identified 13,493 suspected bot accounts, amongst which Twitter found only 1% as being linked to Russia. In our referendum tweet dataset there are tweets by 1,808,031 users in total, which makes the City bot accounts only 0.74% of the total.

If we consider in particular, Twitter accounts that have posted more than 50 times a day (considered high automation accounts by researchers), then there are only 457 such users in the month leading up to the referendum on 3 June 2016.

The most prolific were "ivoteleave" and "ivotestay", both suspended, which were similar in usage pattern. There were also a lot of accounts that did not really seem to post much about Brexit but were using the hashtags in order to get attention for commercial reasons.

We also analysed the leaning of these 457 high automation accounts an identified 361 as pro-leave (with 1,048,919 tweets), 39 pro-remain (156,331 tweets), and the remaining 57 as undecided.

I covered how we can address the “fake news” problem in me previous blog post (link) but in summary we need to promote fact checking efforts, and fund open-source research on automatic methods for disinformation detection.

Disclaimer: All views are my own.

Discerning Truth in the Age of Ubiquitous Disinformation (4): Russian Involvement in the Referendum and the Impact of Social Media Misinformation on Voting Behaviour

Discerning Truth in the Age of Ubiquitous Disinformation (4)

Russian Involvement in the Referendum and the Impact of Social Media Misinformation on Voting Behaviour

Kalina Bontcheva (@kbontcheva)


In my previous blog posts I wrote about the 4Ps of the modern disinformation age: post-truth politics, online propaganda, polarised crowds,  and partisan media; and how we can combat online disinformation. 


The news is currently full of reports of Russian involvement in the referendum and the potential impact of social media misinformation on voting behaviour

A small scale experiment by the Guardian exposed 10 US voters (five on each side) to  alternative Facebook news feeds. Only one participant changed his mind as to how they would vote. Some found their confirmation bias too hard to overcome, while others became acutely aware of being the target of abuse, racism, and misogyny.  A few started empathising with voters holding opposing views. They also gained awareness of the fact that opposing views abound on Facebook, but the platform is filtering them out. 

Russian Involvement in the Referendum

We analysed the accounts that were identified by Twitter as being associated with Russia in front of the US Congress in the fall of 2017, and we also took the other 45 ones that we found with BuzzFeed. We looked at tweets posted by these accounts one month before the referendum, and we did not find an awful lot of activity when compared to the overall number of tweets on the referendum, i.e. both the Russia-linked ads and Twitter accounts did not have major influence. 

There were 3,200 tweets in our data sets coming from those accounts, and 800 of those—about 26%—came from the new 45 accounts that we identified. However, one important aspect that has to be mentioned is that those 45 new accounts were tweeting in German, so even though they are there, the likely impact of those 800 tweets on the British voter is, I would say, not very likely to have been significant.

The accounts that tweeted on 23 Jun were quite different from those that tweeted before or after, with virtually all tweets posted in German. Their behaviour is also very different - with mostly retweets on referendum day by a tight network of anti-Merkel accounts, often within seconds of each other. The findings are in line with those of Prof. Cram from the University of Edinburgh, as reported in the Guardian. 

Journalists from BuzzFeed UK and our Sheffield  team  used the re-tweet  network to identify another 45 suspicious accounts, subsequently suspended by Twitter. Amongst the 3,200 total tweets, 830 came from the 45 newly identified accounts (26%).  Similar to those identified by Twitter, the newly discovered accounts were largely ineffective in skewing public debate. They attracted very few likes and retweets – the most successful message in the sample got just 15 retweets.

An important distinction that needs to be made is between Russia-influenced accounts that used advertising on one hand, and the Russia-related bots found by Twitter and other researchers on the other. 

The Twitter sockpuppet/bot accounts generally pretended to be authentic people (mostly American, some German) and would not resort to advertising, but instead try to go viral or gain prominence through interactions. An example of one such successful account/cyborg is Jenn_Abrams. Here are some details on how the account duped mainstream media:

http://amp.thedailybeast.com/jenna-abrams-russias-clown-troll-princess-duped-the-mainstream-media-and-the-world 

“and illustrates how Russian talking points can seep into American mainstream media without even a single dollar spent on advertising.”

https://www.theguardian.com/technology/shortcuts/2017/nov/03/jenna-abrams-the-trump-loving-twitter-star-who-never-really-existed 

http://money.cnn.com/2017/11/17/media/new-jenna-abrams-account-twitter-russia/index.html 

A related question is the influence of Russia-sponsored media and its Twitter posts. Here we consider the Russia Today promoted tweets - the 3 pre-referendum ones attracted just 53 likes and 52 retweets between them.

We analysed all tweets posted one month before 23 June 2016, which are either authored by Russia Today or Sputnik, or are retweets of these. This gives an indication of how much activity and engagement there was around these accounts. To put these numbers in context, we also included the equivalent statistics for the two main pro-leave and pro-remain Twitter accounts:

Account	Original tweets	Retweeted by others	Retweets by this account	Replies by account	Total tweets
@RT_com -  General Russia Today	39	2,080 times	62	0	2,181
@RTUKnews	78	2,547 times	28	1	2,654
@SputnikInt	148	1,810 times	3	2	1,963
@SputnikNewsUK	87	206 times	8	4	305
TOTAL	352	6,643	101	7	7,103
@Vote_leave	2,313	231,243	1,399	11	234,966
@StrongerIn	2,462	132,201	910	7	135,580

We also analysed which accounts retweeted RT_com and RTUKnews the most in our dataset. The top one with 75 retweets of Russia Today tweets was a self-declared US-based account that retweets Alex Jones from infowars, RT_com, China Xynhua News, Al Jazeera, and an Iranian news account. This account (still live) joined in Feb 2009 and as of 15 December 2017 has 1.09 million tweets - this means an average of more than 300 tweets per day, indicating it is a highly automated account. It has more than 4k followers, but follows only 33 accounts. Two of the next most active retweeters are a deleted and a suspended account, as well as two accounts that both stopped tweeting on 18 Sep 2016. 

For the two Sputnik accounts, the top retweeter made 65 retweets. It declares itself as Ireland based; has 63.7k tweets and 19.6k likes; many self-authored tweets; last active on 2 May 2017; account created on May 2015; avg 87 tweets a day (which possibly indicates an automated account);. It also retweeted Russia Today 15 times. The next two Sputnik retweeters (61 and 59 retweets respectively) are accounts with high average post-per-day rate (350 and 1,000 respectively) and over 11k and 2k followers respectively. Lastly, four of the top 10 accounts have been suspended or deleted. 

Disclaimer: All views are my own.