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Our active research projects use advanced analytics to help better secure our community through the use of various technologies and tools, including Natural Language Processing (NLP).

Natural Language Processing

Social media presents a wealth of opportunities for Big Data research. The medium is heavily used for advertising, political outreach, as a source of news, and by various groups and individuals to spread their messages and ideologies. Social media data can be used to uncover security challenges and counter violent extremism by conducting sentiment analysis of the statements made by political allies and foes. It also poses unique challenges and complexities, including the lack of analysis of the attitudes expressed online, and the interconnection between violent extremism events and attitudinal expressions.

IAAS is addressing this challenge through the development of algorithms that quantify the attitudes in social media, and that also assess, predict, and influence related political network structures.

 

Determinants of Firearms Legislation

Researchers: Iliyan Iliev, Joshua Hill, Jonathon Evans

While it is unclear whether the recent rise in deaths by firearms is an artifact or a trend, the importance of examining the topic of shootings in the US (and elsewhere) remains unchanged. Much of the existing research focuses on methods to reduce firearm deaths or incidents, and while this is essential, understanding how these incidents impact the legislative process is key to understanding how to improve the chances of both finding effective legislation and getting that legislation passed.

IAAS’ novel approach uses an NLP method to determine both the type of legislation introduced to deal with firearms as well as, when combined with a temporal analysis, the effects of a number of existing factors on both the passage of legislation, gun crimes, and other variables important to the legislative process.

 

Missing Data and Security

Researchers: Joshua Hill, Manoj Vellatoori

Missing data remains a tremendous challenge within a significant number of domains, and understanding the best methods of augmenting or replacing missing data is an important area of research. Additionally, while data replacement is an important problem in itself, there are a number of applications to problems within a security context – not least of which is incident prediction.

IAAS is examining new methods of data imputation through machine learning techniques as well as attempting to create a standard framework for testing imputations that will have applicability across fields. We are additionally developing novel applications of machine learning techniques to existing security problems within the same problem framework as missing data issues.

 

Security and Sports Management

Researchers: Justin Kurland, Joshua Hill

Security has rightly become an increasing concern for the sports and entertainment industries. However, despite the importance of the topic, there has yet to be a comprehensive assessment of the research within the sports management literature regarding the topic of security. Even less is known regarding what types of empirical research have been conducted or what statistical techniques have been employed.

IAAS is addressing this gap in partnership with the National Center for Spectator Sports Safety and Security (NCS4) by engaging in a systematic review of the existing sports management literature to determine how much is known, and remains unknown, in relation to security within sports management.