New analytics for measuring and countering social influence and persuasion of extremist groups
Principal Investigator: Hasan Davulcu, Arizona State University
Co-Investigators: Mark Woodward, Paolo Shakarian, Baoxin Li (Arizona State University)
Years of Award: 2015 - 2019
Managing Service Agency: Office of Naval Research
This project aims to develop novel measurement and analytic methods for detecting Information Cascades (ICs) and automated approaches informed by social science to determine what types of information “goes viral” and under what circumstances. Online Information Cascades (ICs) in which tens and in some case hundreds of thousands of individuals participate to spread information and opinions across the globe are now common. ICs point to moments of heightened resonance among individuals and groups that often lead to collective behaviors. Substantively we will focus on the diffusion of violent and counter-violent ideologies in Muslim communities. Within each of these objectives, we also explore relationships between online ICs and those in natural environments. Our design builds fundamental knowledge of social media tools that could be used to mitigate the influence of extremist groups. We also develop new means of data gathering and analysis that the intelligence community could employ to better understand the mindset and motivations of non-state adversaries and their supporters.
Owl in the Olive Tree posts:
Woodward, Mark and Sani Umar, Muhammad. 2019. Culture as Counter Extremism: West African, European, and Southeast Asian Cases. July 9.
- Salehi, A., Ozer, M., Davulcu, H. 2018. Sentiment-driven Community Profiling and Detection on Social Media, Proc. of the 29th on ACM Hypertext and Social Media (HT’18), pp. 229-237, Baltimore, MD.
- Salehi, A., Davulcu, H. 2018. Detecting Antagonistic and Allied Communities on Social Media, IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM’18), pp. 99-106, Barcelona, Spain.
- Ozer, M., Yildirim, Y., Davulcu, H. 2017. Negative Link Prediction and Its Applications in Online Political Networks, ACM Conference on Hypertext and Social Media (ACM Hypertext 17), Prague, Czech Republic.
- E. Marin, R. Guo, P. Shakarian. 2017. Temporal Analysis of Influence to Predict User's Adoption in Online Social Networks, International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction (SBP-BRiMS) 2017.
- Y. Li, S. Hu and B. Li. 2016. “Recognizing Unseen Action in a Domain-adaptive Embedding Space”, International Conference on Image Processing