Anonymized Social Media Datasets, Dark Networks, and Strategic Simulation

Transcription

1 Anonymized Social Media Datasets, Dark Networks, and Strategic Simulation Corey Lofdahl Frank Witmer Prepared for the conference on, Human Security in The New Information Age: How Social Media Impacts Military Missions, Government to Government Coordination, and International Relationships October 8-9, 2013 Renaissance Hotel Brussels, Belgium

2 Social Media The use of social media has exploded Facebook Twitter LinkedIn Etc. It s hard to access social media data due to privacy concerns Data is necessary for social media tool and analysis algorithm development Data is owned by private companies Naïve solution: anonymize datasets by removing personally identifiable information (PII) 2 of 15

3 Anonymized and Anonymous Datasets Some anonymized datasets have been made available Anonymized datasets have had their personal data removed De-anonymization attacks leverage similar structure from similar networks to re-instantiate anonymized data De-anonymization attacks make companies even more leery and cautious about sharing their data Generating anonymous networks would be useful Anonymous networks are impossible to de-anonymize Could generate networks of a certain size or type to test algorithms Research Question: How to generate anonymous networks? 3 of 15

4 Synthetic, Anonymous Network Challenge(s) The three main technical challenges to support the generation of synthetic, anonymous networks: 1. Must be rooted in complex human social behavior as represented by social and behavioral theories 2. Synthetic and real-world networks must be comparable 3. Significant differences will exist between real and synthetic networks Some form of guidance, process, or tool must be provided to improve the quality of generated synthetic networks 4 of 15

5 Development Goals 1. Generate synthetic anonymous networks Based on behavioral antecedents 2. Understand evolution of empirical networks Based on network comparisons 3. Use network simulation to inform GIS-based visualizations and operational support representations Enables strategic analysis Enables HA/DR analysis I.e., apply limited resources 5 of 15

6 Subject Matter: Methodology: Strategic Special Operations Dark Networks Strategic Special Operations Forces (SOF) are tactically excellent Achieving strategic effects remains an opportunity System dynamics (SD) simulation supports strategic analysis over time but not space Network models characterize space but not time Agent-Based (AB) simulations that incorporate social network (ABSN) representations can characterize system change over space and time Using dark (insurgent, criminal, terrorist) network disruption as a use case 6 of 15 Use dark networks to inform strategic analysis

7 Special Operations and Network Analysis Tactical Kinetic Based on centrality metrics Focused on changes to few nodes Short-term Strategic Non-kinetic Based on richer set of metrics and diffusion models Focused on changes to multiple links (influence) Long-term 7 of 15

8 Tactical Centrality and Node Removal Analysis 8 of 15 Methodology drives kinetic tactics (Everton 2013)

9 Strategic Methodology: AB and SD Model Specification Overview, Design concepts, & Details (ODD) Purpose Entities, states variables, and scales Process overview and scheduling Design concepts Basic principles Interaction Emergence Stochasticity Adaptation Collectives Objectives Observation Learning Prediction Sensing Initialization Input data Submodels SD Reference Mode State theory State ancillary theory Identify 7/-2 variables Graph them over time Postulate causality Create model Run, test, iterate 9 of 15 Specify hard AB models with simple SD models

10 Moving from SD to AB Model Specification SD: (1) causality and (2) meta-networks income resources expenditures legitimacy tech investment investment recruits technology infrastructure Member Networks Dark Network Capacity - tech depreciation - depreciation - attrition system shock Integrated AB: Member Networks Differentiated 10 of 15 Move from aggregate to disaggregated models

11 Macrodynamics from Microstructure Macrodynamics Microstructure Integrated Efficiency Member Networks Differentiated Robustness Structural disaggregation impacts dynamics 11 of 15

12 Aggregate Dark Network AB Analysis 3 insurgencies modeled with varying network distributions from integrated to differentiated: Integrated networks are efficient and nonresilient Differentiated networks are inefficient and robust Robust Rebounding Nonresilient 12 of 15 System structure drives dynamics

13 Next Steps: Implementing Strategic Network Analysis SD used to specify AB models Explicit causality Meta-networks (Carley) Social media one of many networks Aggregate system behavior AB used to disaggregate stocks and specify networks Characterize network shape Specify flows in terms of network growth and decay I.e., attachment & detachment income resources Integrated expenditures legitimacy tech investment investment recruits technology infrastructure Member Networks Dark Network Capacity - tech depreciation - depreciation - attrition system shock Differentiated AB results validated against, (1) Behavioral theory (2) Aggregate SD results (3) Empirical reference networks 13 of 15

14 Conclusions SD used to specify aggregate system behavior over time AB leverages networks to model systems over time and space 14 of 15 Anonymous network generation Network evolution Spatial visualization Resulting capability ultimately will enable, Strategic SOF analysis HA/DR analysis I.e., achieving strategic, geographically distributed effects with limited resources

15 Contact Information Charles River Analytics Inc. 625 Mount Auburn Street Cambridge, MA Voice: Fax: Dr. Corey Lofdahl x of 15