Differentiating Violent Extremist Offenders using Linguistic Analysis

• In Event: NIJ Day: Online Interactions Leading to Radicalization: Mobilization to Violent Extremism and Internet Safety

Abstract

The goal of this study is to better understand the degree to which linguistic risk factors can be used to predict violent extremist outcomes at the individual level. Furthermore, it seeks to identify the degree to which these linguistic risk factors vary between actor ideologies, between actor types, and within the same actor over time. The project is identifying and collecting data from individuals who posted expressions of ideology online, including at least (1) 200 people with violent extremist offenses, (2) 200 matched people with nonviolent extremism offenses, (3) 200 non-extremist, partisan social media users who posted partisan social media content and who will serve as controls to extremists to identify transitions to extremism, (4) 100 people with far-right extremism offenses, (5) 100 people with leftwing extremism offenses, (6) 100 people with religious extremism offenses, (7) 100 lone-actors, and (8) 100 people with group-based offenses. This overall sample will cover the spectrum of violent extremist actors; including lone actors and cell-based actors who operated within the United States since 2000, individuals who departed the United States to join foreign insurgencies, and group-based opportunistic actors (e.g., those who participated in the January 6 Capitol riot) who committed extremist violence during a larger public event. This data will then be analyzed using a psychologically validated linguistic text analysis program (Linguistic Inquiry Word Count; LIWC) to identify domestic extremism and violent extremist plots. The research will examine different LIWC characteristics across actor ideologies and actor types, and detect the risk of transitioning to extremism using an analysis of longitudinal linguistic behavior.

Author

• Neil Shortland, University of Massachusetts Lowell