Sheffield researchers develop algorithm to predict which Twitter users will spread fake news before they do it

The artificial intelligence algorithm is capable of predicting whether a social media user is likely to share content from unreliable news sources before they post so called ‘fake news’.

A team of researchers from the university’s Department of Computer Science, led by PhD student Yida Mu and Dr Nikos Aletras, analysed more than a million tweets from approximately 6,200 Twitter users by developing new natural language processing methods to help computers process and understand large amounts of data.

The tweets they studied were all posts that were publicly available for anyone to see on the social media platform.

Twitter users were grouped into two categories as part of the study, which has been published in the journal PeerJ, and those who have shared unreliable news sources and those who only share stories from reliable news sources were separated.

The data was then used to train a machine-learning algorithm that can accurately predict (79.7 per cent) whether a user will re-post content from unreliable sources at some point in the future.

Results from the ‘Identifying Twitter users who repost unreliable news sources with linguistic information’ study found that the Twitter users who shared stories from unreliable sources are more likely to tweet about either politics or religion and use impolite language.

They often posted tweets with words such as ‘liberal’, ‘government’, ‘media’, and their tweets often related to politics in the Middle East and Islam, with their tweets often mentioning ‘Islam’ or ‘Israel’.

In contrast, the study found that Twitter users who shared stories from reliable news sources often tweeted about their personal life, such as their emotions and interactions with friends.

This group of users often posted tweets with words such as ‘mood’. ‘wanna’, ‘gonna’, ‘I’ll’, ‘excited’, and ‘birthday’.

Findings from the study could help social media companies such as Twitter and Facebook develop ways to tackle the spread of disinformation online and aid social scientists and psychologists in their understanding of user behaviour on a large scale.

Lecturer in Natural Language Processing at the University of Sheffield Dr Nikos Aletras said: “Social media has become one of the most popular ways that people access the news, with millions of users turning to platforms such as Twitter and Facebook every day to find out about key events that are happening both at home and around the world.

"However, social media has become the primary platform for spreading disinformation, which is having a huge impact on society and can influence people’s judgement of what is happening in the world around them.

“As part of our study, we identified certain trends in user behaviour that could help with those efforts - for example, we found that users who are most likely to share news stories from unreliable sources often tweet about politics or religion, whereas those who share stories from reliable news sources often tweeted about their personal lives.

“We also found that the correlation between the use of impolite language and the spread of unreliable content can be attributed to high online political hostility.”

To access the paper in full, click here.

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