Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states

Exploring the role of sentiments in identification of active and influential bloggers

The social Web provides opportunities for the public to have social interactions and online discussions. A large number of online users using the social web sites create a high volume of data. This leads to the emergence of Big Data, which focuses on computational analysis of data to reveal patterns, and associations relating to human interactions. Such analyses have vast applications in various fields such as understanding human behaviors, studying culture influence, and promoting online marketing. The blogs are one of the social web channels that offer a way to discuss various topics. Finding the top bloggers has been a major research problem in the research domain of the social web and big data. Various models and metrics have been proposed to find important blog users in the blogosphere community. In this work, first find the sentiment of blog posts, then we find the active and influential bloggers. Then, we compute various measures to explore the correlation between the sentiment and active as well as bloggers who have impact on other bloggers in online communities. Data computed using the real world blog data reveal that the sentiment is an important factor and should be considered as a feature for finding top bloggers. Sentiment analysis helps to understand how it affects human behaviors

Exploring the role of sentiments in identification of active and influential bloggers

The social Web provides opportunities for the public to have social interactions and online discussions. A large number of online users using the social web sites create a high volume of data. This leads to the emergence of Big Data, which focuses on computational analysis of data to reveal patterns, and associations relating to human interactions. Such analyses have vast applications in various fields such as understanding human behaviors, studying culture influence, and promoting online marketing. The blogs are one of the social web channels that offer a way to discuss various topics. Finding the top bloggers has been a major research problem in the research domain of the social web and big data. Various models and metrics have been proposed to find important blog users in the blogosphere community. In this work, first find the sentiment of blog posts, then we find the active and influential bloggers. Then, we compute various measures to explore the correlation between the sentiment and active as well as bloggers who have impact on other bloggers in online communities. Data computed using the real world blog data reveal that the sentiment is an important factor and should be considered as a feature for finding top bloggers. Sentiment analysis helps to understand how it affects human behaviors

Strategic latency and world order

The article of record as published may be found at http://dx.doi.org/10.1016/j.orbis.2010.10.006This article examines "strategic latency", a condition in which technologies that could provide military (or economic) advantage remain untapped. As difficult as it is to explain why certain ideas and technologies flourish and find rapid acceptance, it is equally hard to understand why some good ideas languish, only to be rediscovered and exploited under other circumstances. Why is latent capacity so often dormant? What are the indicators that latent capacity is on the verge of being weaponized

Use of a Fast Information Extraction Method as a Decision Support Tool

Ad-hoc extraction of information from documents can ensure the transparency of decisions made by an organization. Different Information Extraction methods have been applied to extract information from various domains. Most widely known methods use manually annotated training documents that require high development time. The automated training methods are not scalable to large application domains. We have developed a semi-automated knowledge-engineering method for building the knowledge-base with minimal efforts. Because our method reduces manual processing of the training data, the development process is very fast. We have developed a prototype application to extract information from the project-reports of the American Recovery and Reinvestment Act (ARRA) of 2009. The fast development process of our system, its scalability to large application domains, and its high extraction effectiveness will help the transparency of management decisions by extracting and mining relevant information

Al Qaeda at the bar: coordinating ideologues and mercenaries in terrorist organizations

Most terrorist groups have limited lifespans. A number of scholars and casual observers have noted that terrorist organizations often are comprised of two types of participants: ideologues or "true believers" dedicated to the group's cause, and mercenaries, who are adept at raising money through illegal means. The latter are interested primarily in their personal gains and have relatively little ideological commitment. Terrorist groups need both participants in order to function effectively. The purpose of the study is to understand the impact of communication on the compositions of terrorist groups. Three experimental treatments consider a coordination problem, and focus on the behavior of the mercenaries. Participants choose whether or not to participate in a terrorist attack. Payoffs are U-shaped in the number of participants, and increase with the number of successful attacks. The treatments allow communication between a leader and frontline fighters ("leader" treatment) or among the frontline fighters themselves ("communication" treatment). In the first treatment, a group leader can post messages to the members, which has a 19 % coordination success rate. For the communication treatment, all participants can post messages anonymously to each other, which yields a 27 % coordination success rate. By contrast, the baseline ("no communication" treatment) shows a success rate of 11 %. We conclude from our experimental evidence that disrupting communications among the frontline fighters is more effective in terminating terrorist organizations