Measuring the Popular Resonance of Daesh’s Propoganda

We describe an innovative approach to social media analysis, combining corpus linguists and statistical methods to measure the resonance of Daesh's propaganda to a sample population (Eqypt). The findings from this research effort demonstrate that: (1) Daesh's messaging is measurable and distinct from other Salafi groups, such as the Egyptian Muslim Brotherhood; (2) while Daesh’s messaging generally do not resonate with Egyptians, its uptake increased in Upper Egypt and the Sinai regions during 2014; and (3) this method can be applied more broadly to measure the spread of violent extremist messaging across regional populations over time. Authors William M. Marcellino, Kim Cragin, Joshua Mendelsohn, Andrew Micahel Cady, Madeline Magnuson, and Kathleen Reedy This article is available in Journal of Strategic Security: https://scholarcommons.usf.edu/jss/vol10/ iss1/4

[1]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[2]  Jeffrey A. Collins Variations in Written English , 2003 .

[3]  K. Smith,et al.  Textual Analysis of Tobacco Editorials: How are Key Media Gatekeepers Framing the Issues? , 2005, American journal of health promotion : AJHP.

[4]  E. Hoff Interpreting the early language trajectories of children from low-SES and language minority homes: implications for closing achievement gaps. , 2013, Developmental psychology.

[5]  Aaron Y. Zelin Picture Or It Didn’t Happen: A Snapshot of the Islamic State’s Official Media Output , 2015 .

[6]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.

[7]  Lisa Kaati,et al.  Detecting Linguistic Markers for Radical Violence in Social Media , 2014 .

[8]  J. Klausen Tweeting the Jihad: Social Media Networks of Western Foreign Fighters in Syria and Iraq , 2015 .

[9]  Melissa J. Krauss,et al.  Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle , 2014, Journal of medical Internet research.

[10]  M. McKinney,et al.  Social Watching a 2012 Republican Presidential Primary Debate , 2014 .

[11]  Brian S. Butler,et al.  Rhetoric and the arts of design , 1996 .

[12]  W. Marcellino,et al.  Talk like a Marine: USMC linguistic acculturation and civil–military argument , 2014 .

[13]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.

[14]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[15]  A. Arvidsson,et al.  Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data , 2014 .

[16]  Arvind Gupta,et al.  A Quantitative Assessment on 26/11 Mumbai Attack using Social Network Analysis , 2011 .

[17]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[18]  D. Kaufer,et al.  A first for women in the kingdom: Arab/west representations of female trendsetters in Saudi Arabia , 2009 .

[19]  Deepa S. Reddy Capturing Hindutva: Rhetorics and Strategies , 2011 .

[20]  Matt Golder,et al.  Who “Wins”? Determining the Party of the Prime Minister , 2011 .