Liam R. McGrath

Automating Frame Analysis

Frame Analysis has come to play an increasingly stronger role in the study of social movements in Sociology and Political Science. While significant steps have been made in providing a theory of frames and framing, a systematic characterization of the frame concept is still largely lacking and there are no recognized criteria and methods that can be used to identify and marshal frame evidence reliably and in a time and cost effective manner. Consequently, current Frame Analysis work is still too reliant on manual annotation and subjective interpretation. The goal of this paper is to present an approach to the representation, acquisition and analysis of frame evidence which leverages Content Analysis, Information Extraction and Semantic Search methods to automate frame annotation and provide a systematic treatment of Frame Analysis.

Towards efficient collaboration in cyber security

Cyber security analysts in different geographical and organizational domains are often largely tasked with similar duties, albeit with domain-specific variations. These analysts necessarily perform much of the same work independently- for instance, analyzing the same list of security bulletins released by largely the same set of software vendors. As such, communication and collaboration between such analysts would be mutually beneficial to the analysts involved, potentially reducing redundancy and offering the opportunity to preemptively alert each other to high-severity security alerts in a more timely fashion. However, several barriers to practical and efficient collaboration exist, and consequently, no such framework exists to support these efforts. In this paper, we discuss the inherent difficulties which make efficient collaboration between cyber security analysts a difficult goal to achieve. We discuss preliminary ideas and concepts towards a collaborative cyber-security framework currently under development, whose goal is to facilitate analyst collaboration across these boundaries. While still in its early stages, we describe work-in-progress towards achieving this goal, including motivation, functionality, concepts, and a high-level description of the proposed system architecture.

Knowledge encapsulation framework for technosocial predictive modeling

Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and content analysis within a collaborative environment, with a functional interface to models and simulations. Existing evidence extraction tools provide some, but not all, the critical components that would empower such an integrated knowledge management environment. This paper describes a novel evidence marshaling solution that significantly advances the state of the art. Its embodiment, the Knowledge Encapsulation Framework (KEF), offers a suite of semi-automated and configurable content harvesting, vetting, annotation and analysis capabilities within a wiki-enabled and user-friendly visual interface that supports collaborative work across distributed teams of analysts. After a summarization of related work, our motivation, and the technical implementation of KEF, we will explore the model for using KEF and results of our research.

Sociocultural predictive analytics and terrorism deterrence

The use of predictive analytics to model terrorist rhetoric is highly instrumental in developing a strategy to deter terrorism. Traditional (e.g. Cold-War) deterrence methods are ineffective with terrorist groups such as al Qaida. Terrorists typically regard the prospect of death or loss of property as acceptable consequences of their struggle. Deterrence by threat of punishment is therefore fruitless. On the other hand, isolating terrorists from the community that may sympathize with their cause can have a decisive deterring outcome. Without the moral backing of a supportive audience, terrorism cannot be successfully framed as a justifiable political strategy and recruiting is curtailed. Ultimately, terrorism deterrence is more effectively enforced by exerting influence to neutralize the communicative reach of terrorists.

Violent frames in action

We present a computational approach to radical rhetoric that leverages the co-expression of rhetoric and action features in discourse to identify violent intent. The approach combines text mining and machine learning techniques with insights from Frame Analysis and theories that explain the emergence of violence in terms of moral disengagement, the violation of sacred values and social isolation in order to build computational models that identify messages from terrorist sources and estimate their proximity to an attack. We discuss a specific application of this approach to a body of documents from and about radical and terrorist groups in the Middle East and present the results achieved.

Agent-Based Knowledge Discovery for Modeling & Simulation

This paper describes extending the automated discovery mechanism of the Knowledge Encapsulation Framework (KEF) through the use of agent technology. KEF is a suite of tools to enable the linking of knowledge inputs (relevant, domain-specific evidence) to modeling and simulation projects, as well as other domains that require an effective collaborative workspace for knowledge-based tasks. This framework can be used to capture evidence (e.g., trusted material such as journal articles and government reports), discover new evidence (covering both trusted and social media), enable discussions surrounding domain-specific topics and provide automatically generated semantic annotations for improved corpus investigation. The current KEF design is described along with the new agent based knowledge management system, which addresses the weaknesses of the current knowledge acquisition approach.

Pattern discovery using semantic network analysis

Cognitive information processing at higher conceptual levels requires a computational approach to knowledge representation and analysis. Semantic network analysis bridges the gap between probabilistic pattern recognition techniques and symbolic representations by replacing cumbersome and computationally complex forms of logic-based semantic inference common in symbolic approaches with mathematical metrics on graph representations of labelled, directed semantic networked data. These metrics in turn support assessment of evidentiary support for the presence of patterns of interest in which entities play specified roles in complex event scenarios. The resulting system allows patterns to be specified at higher levels of conceptual abstraction while also remaining robust to conflicting and incomplete information.

Semantic network analysis for evidence evaluation: The threat anticipation initiative

Semantic network analysis offers a computational method for discovery, pattern matching, and reasoning with large amounts of unstructured, semi-structured and structured information. The Threat Anticipation Platform replaces more cumbersome and computationally complex forms of semantic inference with metrics on graph representations of labeled, directed semantic networked data to identify the degree of evidence within multiple data sources for specified hypotheses about potential events.

Trends & Controversies: Sociocultural Predictive Analytics and Terrorism Deterrence

The use of predictive analytics to model terrorist rhetoric is highly instrumental in developing a strategy to deter terrorism. Traditional (e.g. Cold-War) deterrence methods are ineffective with terrorist groups such as al Qaida. Terrorists typically regard the prospect of death or loss of property as acceptable consequences of their struggle. Deterrence by threat of punishment is therefore fruitless. On the other hand, isolating terrorists from the community that may sympathize with their cause can have a decisive deterring outcome. Without the moral backing of a supportive audience, terrorism cannot be successfully framed as a justifiable political strategy and recruiting is curtailed. Ultimately, terrorism deterrence is more effectively enforced by exerting influence to neutralize the communicative reach of terrorists.