Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups

An Actionable Knowledge Representation for Popular Fundamental Investment Strategies

Individual investors consistently underperform relevant investment benchmarks. Consequently, a considerable body of literature of fundamental investment strategies targeted towards this audience emerged. Several online platforms provide operationalizations of these strategies in the form of stock screeners. However, each platform must use its own interpretation of the strategy as no central knowledge repository exists. Arguing that ontologies standardize the concepts relevant to a domain and enable knowledge sharing among domain users, this paper seeks to explore that viability of an ontology as a knowledge representation method to represent fundamental investment strategies. Our efforts herein go beyond representing the concepts and inter-concept relationships that are descriptive of fundamental investment strategies, as we also demonstrate that ontologies using SWRL rules can deploy these strategies as stock pickers (also referred to as stock screening). We use the CANSLIM strategy as a case, modeling and executing it on simulated data using our ontology and SWRL

X-IM Framework to Overcome Semantic Heterogeneity Across XBRL Filings

Semantic heterogeneity in XBRL precludes the full automation of the business reporting pipeline, a key motivation for the SEC’s XBRL mandate. To mitigate this problem, several approaches leveraging Semantic Web technologies have emerged. While some approaches are promising, their mapping accuracy in resolving semantic heterogeneity must be improved to realize the promised benefits of XBRL. Considering this limitation and following the design science research methodology (DSRM), we develop a novel framework, XBRL indexing-based mapping (X-IM), which takes advantage of the representational model of representation theory to map heterogeneous XBRL elements across diverse XBRL filings. The application of representation theory to the design process informs the use of XBRL label linkbases as a repository of regularities constitutive of the relationships between financial item names and the concepts they describe along a set of equivalent financial terms of interest to investors. The instantiated design artifact is thoroughly evaluated using standard information retrieval metrics. Our experiments show that X-IM significantly outperforms existing methods

Leveraging XBRL Calculation Linkbases to Overcome Semantic Heterogeneity across XBRL Fillings: The Multi-Ontology Multi-Concept Matrix (M3)

In 2008, the US Securities and Exchange Commission (SEC) mandated that large accelerated filers issue financial statements in eXtensible Business Reporting Language (XBRL). One purported benefit of issuing financial statements in a machine readable language is the facilitation of automated inter firm comparisons. However, XBRL, an XML-based language, is extensible by each individual filer. This extensibility compromises the ability of an automated agent to make meaningful comparisons between firms on any given metric or financial concept by introducing semantic heterogeneity across filings. Our major premise in this paper is that the meaning of a given financial concept is captured by its relative position in a calculation hierarchy. The representation of calculation linkbases using an ontology language thus becomes the basis of our efforts towards the resolution of semantic heterogeneity across XBRL filings in the US jurisdiction