Researchers analyzed Russian social media data and conducted interviews with regional and security experts to understand the critical ingredients to countering Russia's propaganda campaign against former Soviet states.

This report defines and describes will to fight and provides a model of unit will to fight that can be applied to ground combat units of any scale. It also provides a theoretical basis for adding will to fight to military war gaming.

To support the U.S. Department of Defense in expanding its capacity for social media analysis, this report reviews the analytic approaches that will be most valuable for information operations and considerations for implementation.

This report documents research focused on helping the Department of Defense build a more-systematic approach to hazing prevention and response. The report documents theory and research on the root causes of hazing and findings and recommendations regarding how best to define hazing, practices to prevent and respond to incidents of hazing, and how the armed forces can improve the tracking of hazing incidents.

Using linguistic and rhetorical theory, researchers developed an improved model of machine-learning technology to detect conspiracy theory language. This report describes the results and suggests ways to counter effects of online conspiracy theories.

This report assesses challenges for unit cohesion from integrating women into special operations forces and provides analytical support for validating occupational standards for positions controlled by U.S. Special Operations Command.

The authors reviewed literature on White identity terrorism and racially or ethnically motivated violent extremism (REMVE) and analyzed social media data from six platforms that host extremist content. They developed a network map that evaluates REMVE network construction, connectivity, geographic location, and proclivity to violence and found that users in the United States are overwhelmingly responsible for REMVE discourse online.

This reference document presents a collection of lessons learned by practitioners from RAND Corporation projects that employed natural language processing (NLP) tools and methods. NLP is an umbrella term for the range of tools and methods that enable computers to analyze human language. The descriptions of lessons learned are organized around four steps: data collection, data processing (i.e., NLP-specific text processing in preparation for modeling), modeling, and application development and deployment. These NLP practitioners spend or spent a majority of their time at RAND working on projects related to national defense, national intelligence, international security, or homeland security; thus, the lessons learned are drawn largely from projects in these areas. Although few of the lessons are applicable exclusively to the U.S. Department of Defense and its NLP tasks, many may prove particularly salient for the department, because its terminology is very domain-specific and full of jargon, much of its data are classified or sensitive, its computing environment is more restricted, and its information systems are generally not designed to support large-scale analysis.