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Terrorism and ideologically inspired violence represent persistent and serious threats to U.S. national security. The January 6, 2021, attack at the U.S. Capitol and other recent events emphasize the need for more research to inform prevention and deradicalization strategies. In this report, the authors aim to characterize why and how individuals joined extremist organizations, as well as how some of them exited these groups. Semistructured interviews were conducted with former extremists and their family members, representing 32 unique stories of 24 white supremacists and eight Islamic extremists. Exposure to propaganda on the internet, in music, and in books and literature was present in more than two-thirds of the sample. Although formal, top-down recruitment occurred for three Islamic extremists, the majority of white supremacists actively sought out participation in extremist organizations. Among the sample, 26 had exited the organizations; of those, six were still undergoing cognitive and emotional deradicalization. Among those who exited, 22 mentioned that a person or group intervened to help them by providing diverse cultural and demographic exposures, emotional support, financial stability, or domestic stability. Interviewees also addressed such systemic issues as unemployment and the need for more-affordable and easily accessible mental health care. These interviews led to recommendations for both research and practice that emphasize the importance of incorporating the voices of those with personal experience and knowledge of ideological extremism into future research designs and efforts to prevent radicalization and promote deradicalization.

Radicalism --- 2000-2099 --- United States.

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The global spread of coronavirus disease 2019 (COVID-19) created a fertile ground for attempts to influence and destabilize different populations and countries. Both Russia and China appear to have employed information manipulation during the COVID-19 pandemic in service to their respective global agendas. This report uses exploratory qualitative analysis to systematically describe the types of COVID-19-related malign and subversive information efforts with which Russia- and China-associated outlets appear to have targeted U.S. audiences from January 2020 to July 2020 and organizes them into a framework. This work lays the foundation for a better understanding of how and whether Russia and China might act and coordinate in the domain of malign and subversive information efforts in the future. This report is the first in a series that will use big data, computational linguistics, and machine learning to test findings and hypotheses generated by the initial analysis. This report is part of RAND's Countering Truth Decay initiative, which considers the diminishing role of facts and analysis in political and civil discourse and the policymaking process. Disinformation and its rampant spread online and offline is one of the key drivers of Truth Decay. Agents—notably such foreign actors as Russia and China and their proxies—fuel and contribute to the explosion in disinformation observed over the past decade. Knowing how Russia and China operate in this space can help inform our understanding of the Truth Decay phenomenon and efforts to mitigate it.

Big data --- COVID-19 (Disease) --- Propaganda, Anti-American --- Propaganda, Russian. --- Propaganda, Chinese. --- Social aspects --- Political aspects --- China. --- Russia. --- United States.

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How can the success (or failure) of Army special operations missions be assessed? The authors develop a methodology for doing so that relies on operational, intelligence, and ambient (publicly available) data, since operational level special operations commands often lack robust staff and resources to generate assessment-specific information. The method assesses the plan's lines of effort and their objectives, develops relevant measures of effectiveness and indicators, and gathers appropriate qualitative and quantitative data. The resulting analysis is presented to the appropriate commander, who can then use the information to adjust lines of effort or activities and other elements. The seven-step process is illustrated through a fictional scenario. Implementation will include incorporating the approach into doctrine and training and standing up an assessment cell. The cell requires access to data streams and appropriate analytic platforms and reachback support.

Special operations (Military science) --- Special forces (Military science) --- Evaluation.

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Policymakers and researchers are increasingly concerned that the U.S. veteran community is at increased risk of radicalization to violent extremism. Although subsequently revised downward, early reports suggested that as many as one in five Capitol Hill attackers was currently or had previously been affiliated with the U.S. military. Extremist groups actively target military members and veterans for recruitment targets because of their training and operational, logistic, and leadership skills. The unique and often lonely experience of leaving the military has been hypothesized to make veterans susceptible to such recruitment. To help address these concerns, the authors conducted a nationally representative survey of veterans to examine the prevalence of support for specific extremist groups and ideologies, including support for political violence. The authors compared their results with those from surveys of the general population. Among other findings, the veteran community, as a whole, did not manifest higher support than the general population. Interestingly, the majority of those who supported political violence were not also supporters of specific groups. A separate appendix provides cross-tabulation data.

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The authors describe an approach for leveraging machine learning to support assessment of military operations. They demonstrate how machine learning can be used to rapidly and systematically extract assessment-relevant insights from unstructured text available in intelligence reporting, operational reporting, and traditional and social media. These data, already collected by operational-level headquarters, are often the best available source of information about the local population and enemy and partner forces but are rarely included in assessment because they are not structured in a way that is easily amenable to analysis. The machine learning approach described in this report helps overcome this challenge. The approach described in this report, which the authors illustrate using the recently concluded campaign against the Lord's Resistance Army, enables assessment teams to provide commanders with near-real-time insights about a campaign that are objective and statistically relevant. This machine learning approach may be particularly beneficial in campaigns with limited or no assessment-specific data, common in campaigns with limited resources or in denied areas. This application of machine learning should be feasible for most assessment teams and can be implemented with publicly and freely available machine learning tools pre-authorized for use on U.S. Department of Defense systems.