Human-machine detection of online-based malign information


As social media is increasingly being used as people’s primary source for news online, there is a rising threat from the spread of malign and false information. With an absence of human editors in news feeds and a growth of artificial online activity, it has become easier for various actors to manipulate the news that people consume. Finding an effective way to detect malign information online is an important part of addressing this issue. RAND Europe was commissioned by the UK Ministry of Defence’s (MOD) Defence and Security Accelerator (DASA) to develop a method for detecting the malign use of information online. The study was contracted as part of DASA’s efforts to help the UK MOD develop its behavioural analytics capability.

Our study found that online communities are increasingly being exposed to junk news, cyber bullying activity, terrorist propaganda, and political reputation boosting or smearing campaigns. These activities are undertaken by synthetic accounts and human users, including online trolls, political leaders, far-left or far-right individuals, national adversaries and extremist groups. In support of government efforts to detect and counter these activities, the research team successfully developed and applied a machine learning model in a Russian troll database to identify differences between authentic political supporters and Russian trolls shaping online debates regarding the 2016 US presidential election. To trial the model’s portability, a promising next step could be to test the model in a new context such as the online Brexit debate.

The research described in this report was commissioned by the UK Ministry of Defence’s Defence and Security Accelerator (DASA) and conducted by RAND Europe.

This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

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