Of course the GOP knows this and it is assuredly the reason they have tirelessly worked to block any efforts to protect America and prevent future Russian attacks on our electoral system. They are traitors betraying our country right before our eyes. Discussion and conclusions Here we have (a) examined the timing of the IRA Twitter activity, which suggests a strategic release in parallel with significant political events before the 2016 election and (b) used vector autoregression (VAR) to test if the success of IRA activity on Twitter predicted changes in the 2016 election opinion polls. On a weekly time scale, we find that multiple time series of IRA tweet success robustly predicted increasing opinion polls for one candidate, but not the other. The opinion polls do not predict future success of the IRA tweets. The findings proved robust to many different checks. The result, a one percent poll increase for the Republican candidate for every 25,000 weekly re-tweets of IRA messages, raises two questions about the effect: one regarding the magnitude and one regarding its asymmetry. Here we have tested prediction, not causality. It seems unlikely that 25,000 re-tweets could influence one percent of the electorate in isolation (Guess, et al., 2019; Allcott, et al., 2019), although this might be more plausible than presumed at first glance, given that only about four percent of viewed tweets result in re-retweets (Lee, et al., 2015), such that 25,000 re-tweets could imply about 500,000 exposures to those messages per week. It is more likely, however, that Twitter is just a subset of a larger disinformation campaign carried out on multiple social media platforms (Issac and Wakabayashi, 2017; Howard, et al., 2018), as well as spread through social contagion (Centola, 2010) and to other parts of the interconnected ‘media ecosystem’ including print, radio and television (Benkler, et al., 2018). In this way IRA disinformation can frame the debate, meaning many more people than those directly exposed can be affected (Jamieson, 2018). Any correlation established by an observational study could be spurious. Though our main finding has proved robust and our time series analysis excludes reverse causation, there could still be a third variable driving the relationship between IRA Twitter success and U.S. election opinion polls. We controlled for one of these — the success of Donald Trump’s personal Twitter account — but there are others that are more difficult to measure; including exposure to the U.S domestic media. The asymmetrical effect we observed could be because specific groups and media outlets were targeted by the IRA (Jamieson, 2018; Miller, 2019) and those media outlets were particularly susceptible to disinformation (Benkler, et al., 2018), leading to considerably more re-tweets from those targeted groups (Badawy, et al., 2018). We use macro-level data to establish a link between exposure to IRA disinformation and changes in U.S. public opinion. However, using aggregated data means we cannot know the extent to which the participants in election polls were exposed to IRA disinformation. This may not matter once social contagion (Centola, 2010) and media ecosystem effects (Benkler, et al., 2018) are taken into consideration. Nonetheless, establishing individual-level causal mechanisms should be a priority (Gerber and Zavisca, 2016; Spaiser, et al., 2017). Here we have presented evidence that social media disinformation can measurably change public opinion polls. Though we focused on a particular high-profile example in 2016, social media propaganda is a growing problem affecting voting populations around the world, regardless of affiliation, and ought to be given serious attention in the future. Our study motivates future investigation that seeks to establish the causal mechanisms of disinformation exposure on the opinions and behavior of individuals. These future studies should measure exposure to all media in the media ecosystem, not just social media.