The 2015–2017 Russian Internet Research Agency (IRA)’s coordinated information operation is one of the earliest and most studied of the social media age. A set of 38 city-specific inauthentic “newsfeeds” made up a large, underanalyzed part of its English-language output. We label 1,000 tweets from the IRA newsfeeds and a matched set of real news sources from those same cities with up to five labels indicating the tweet represents a world in unrest and, if so, of what sort. We train a natural language classifier to extend these labels to 268 k IRA tweets and 1.13 million control tweets. Compared to the controls, tweets from the IRA were 34% more likely to represent unrest, especially crime and identity danger, and this difference jumped to about twice as likely in the months immediately before the election. Agenda setting by media is well-known and well-studied, but this weaponization by a coordinated information operation is novel.

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Calibration of computer models and the use of those models for design are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach on the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized.

Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multiobjective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade.