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The United States has a capability gap in detecting malign or subversive information campaigns before these campaigns substantially influence the attitudes and behaviors of large audiences. Although there is ongoing research into detecting parts of such campaigns (e.g., compromised accounts and "fake news" stories), this report addresses a novel method to detect whole efforts. The authors adapted an existing social media analysis method, combining network analysis and text analysis to map, visualize, and understand the communities interacting on social media. As a case study, they examined whether Russia and its agents might have used Russia's hosting of the 2018 World Cup as a launching point for malign and subversive information efforts. The authors analyzed approximately 69 million tweets, in three languages, about the World Cup in the month before and the month after the event, and they identified what appear to be two distinct Russian information efforts, one aimed at Russian-speaking and one at French-speaking audiences. Notably, the latter specifically targeted the populist gilets jaunes (yellow vests) movement; detecting this effort months before it made headlines illustrates the value of this method. To help others use and develop the method, the authors detail the specifics of their analysis and share lessons learned. Outside entities should be able to replicate the analysis in new contexts with new data sets. Given the importance of detecting malign information efforts on social media, it is hoped that the U.S. government can efficiently and quickly implement this or a similar method.

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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.