U.S. Counterterrorism Narrative: A Way Forward

Abstract

While there seems to be widespread agreement that the U.S. counterterrorism narrative is failing, there is little empirical evidence for what the U.S. counter-narrative strategy since 9/11 has been, nor is there an analytical framework for measuring its success or failure. This thesis investigates the effectiveness of the U.S. counterterrorism narrative strategy in the post-9/11 period (2001 through 2016), and develops an effective U.S. counterterrorism narrative strategy. Content analysis of 75 U.S. presidential speeches and 50 U.S. Department of State Twitter postings, and a measurement of U.S. performative power between 2001 and 2016, demonstrates that only the narrative speech factor of promoting commonality has a negative correlation with terrorist attacks in the United States. More messages that promote commonality correlates to decreased terrorist attacks. To understand when to use this messaging, the social identity analytical method was applied to a U.S. presidential speech and an Islamic State leaders speech and demonstrates that the U.S. government lacks comprehension of social in-group identification nuances. To target messaging effectively, the framework should be applied on a consistent basis, promoting commonality in narratives within a larger comprehensive counterterrorism strategy.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2019
Accession Number
AD1073636

Entities

People

  • Madeline T. Kristoff

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Counterterrorism
  • Department Of State
  • Foreign Relations
  • Geographic Regions
  • Governments
  • Human Population
  • International Relations
  • National Politics
  • National Security
  • Personnel Management
  • Recreation
  • Social Media
  • Societies
  • Terrorism
  • Terrorists
  • United States
  • Warfare

Readers

  • Computational Modeling and Simulation
  • Political Violence and Terrorism Studies.
  • Speech Processing/Speech Recognition.