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David Jensen - One of the best experts on this subject based on the ideXlab platform.
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the application of statistical relational learning to a database of criminal and Terrorist Activity
SIAM International Conference on Data Mining, 2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
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SDM - The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity.
2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
Brian Delaney - One of the best experts on this subject based on the ideXlab platform.
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the application of statistical relational learning to a database of criminal and Terrorist Activity
SIAM International Conference on Data Mining, 2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
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SDM - The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity.
2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
Michael D. Porter - One of the best experts on this subject based on the ideXlab platform.
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Endogenous and Exogenous Effects in Contagion and Diffusion Models of Terrorist Activity
2016Co-Authors: Gentry White, Fabrizio Ruggeri, Michael D. PorterAbstract:The proliferation of terrorism is a serious concern in national and international security, as its spread is seen as an existential threat to Western liberal democracies. Understanding and effectively modelling the spread of terrorism provides useful insight into formulating effective responses. A mathematical model capturing the theoretical constructs of contagion and diffusion is constructed for explaining the spread of Terrorist Activity and used to analyse data from the Global Terrorism Database from 2000--2016 for Afghanistan, Iraq, and Israel.
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Endogenous and exogenous effects in contagion and diffusion models of Terrorist Activity
2016Co-Authors: Gentry White, Fabrizio Ruggeri, Michael D. PorterAbstract:Variation in rates of Terrorist Activity over time is explained via contagion or diffusion. Models for social contagion and diffusion are shown to be cases of the cluster process representation of the Hawkes self-exciting process model. Contagion and diffusion models exploring variations in endogenous and exogenous effects are fitted to data from the Global Terrorism Database for 2000–2015. Model selection criteria are shown to differentiate between contagion and diffusion process and events with high fatalities exhibit less influence on the probability of future events. The practical applications of these results include exploratory modelling and forecasting to inform policy decisions.
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endogenous and exogenous effects in self exciting process models of Terrorist Activity
Institute for Future Environments; School of Mathematical Sciences; Science & Engineering Faculty, 2015Co-Authors: Gentry White, Fabrizio Ruggeri, Michael D. PorterAbstract:A model based on the cluster process representation of the self-exciting process model in White and Porter 2013 and Ruggeri and Soyer 2008is derived to allow for variation in the excitation effects for Terrorist events in a self-exciting or cluster process model. The details of the model derivation and implementation are given and applied to data from the Global Terrorism Database from 2000–2012. Results are discussed in terms of practical interpretation along with implications for a theoretical model paralleling existing criminological theory.
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GPU accelerated MCMC for modeling Terrorist Activity
Computational Statistics & Data Analysis, 2014Co-Authors: Gentry White, Michael D. PorterAbstract:The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model Terrorist Activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.
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Terrorism Risk, Resilience and Volatility: A Comparison of Terrorism Patterns in Three Southeast Asian Countries
Journal of Quantitative Criminology, 2013Co-Authors: Gentry White, Michael D. Porter, Lorraine MazerolleAbstract:Objective This article explores patterns of Terrorist Activity over the period from 2000 through 2010 across three target countries: Indonesia, the Philippines and Thailand. Methods We use self-exciting point process models to create interpretable and replicable metrics for three key terrorism concepts: risk, resilience and volatility, as defined in the context of Terrorist Activity. Results Analysis of the data shows significant and important differences in the risk, volatility and resilience metrics over time across the three countries. For the three countries analysed, we show that risk varied on a scale from 0.005 to 1.61 “expected Terrorist attacks per day”, volatility ranged from 0.820 to 0.994 “additional attacks caused by each attack”, and resilience, as measured by the number of days until risk subsides to a pre-attack level, ranged from 19 to 39 days. We find that of the three countries, Indonesia had the lowest average risk and volatility, and the highest level of resilience, indicative of the relatively sporadic nature of Terrorist Activity in Indonesia. The high terrorism risk and low resilience in the Philippines was a function of the more intense, less clustered pattern of terrorism than what was evident in Indonesia. Conclusions Mathematical models hold great promise for creating replicable, reliable and interpretable “metrics” to key terrorism concepts such as risk, resilience and volatility.
Aaron Mannes - One of the best experts on this subject based on the ideXlab platform.
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testing the snake head strategy does killing or capturing its leaders reduce a Terrorist group s Activity
Social Science Research Network, 2008Co-Authors: Aaron MannesAbstract:Since the attacks of 9/11 the search for effective counter- terror strategies has become an urgent priority for policy makers. Dr. Audrey Kurth Cronin, a Professor at the National War College, has argued that the United States has made numerous missteps in developing counter-terror strategies because of its limited experience with the phenomenon.i Quantitative tests on databases of Terrorist Activity can help us examine the effects of various strategies in order to determine their degree of impact. One strategy that is considered to be effective by conventional wisdom is “decapitation” – the tactic of removing the leadership of Terrorist organizations. Besides its presumed efficacy, the decapitation strategy is also pursued as a matter of justice and in order to reassure the society targeted by Terrorists that its government is taking action on its behalf. This paper tests the effectiveness of the decapitation strategy in terms of the reduction of Terrorist Activity.
Clifford J Weinstein - One of the best experts on this subject based on the ideXlab platform.
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the application of statistical relational learning to a database of criminal and Terrorist Activity
SIAM International Conference on Data Mining, 2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
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SDM - The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity.
2010Co-Authors: Brian Delaney, Andrew S Fast, William M Campbell, Clifford J Weinstein, David JensenAbstract:We apply statistical relational learning to a database of criminal and Terrorist Activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent Activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of Terrorist groups or as input to inform human decision makers. 1 Background and Motivation During the last decade, there has been an increasing effort toward data collection on criminal and terror networks using open source materials (e.g. news articles, police reports, and court documents.) A straightforward use of such data includes manual analysis of groups and individuals involved in nefarious Activity to inform key decision makers tasked with preventing future bombings or other violent attacks. However, if the collection is detailed with specific annotations including continuous variables and categorical fields, the application of statistical machine learning becomes possible. An example of such an analysis is shown in [1], where the author used statistical methods to indentify extremist ∗This work was sponsored by the Department of Defense under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. †MIT Lincoln Laboratory, Information Systems Technology Group ‡University of Massachusetts Amherst, Knowledge Discovery Laboratory. A. Fast now at Elder Research Inc. groups responsible for surprise terror attacks. By modeling past behavior, statistical techniques can help find large scale patterns in the data and possibly be used to prevent or inform future activities. This paper investigates the use of statistical machine learning to predict individual attributes and event outcomes from a graphical representation of a relational database of Terrorist Activity. We apply statistical relational learning algorithms to predict leadership roles of individuals in a group based on patterns of Activity, communication, and individual attributes. Using labeled training data, we apply supervised learning to build a model which describes the structures and patterns of leadership roles. The relational model returns a probability that a particular person is in a leadership role given a graphical representation of the individuals activities and attributes. A held out test set is used for evaluation and receiver operator curves (ROC) for correct prediction of leadership is presented. A more complex model is applied to give improved performance in a more realistic ”data poor” test condition. Such features can be important components of an overall automatic threat detection system such as the one presented in [2]. In such a system, automatic identification of individual roles and activities from basic features can help infer intent of groups and individuals through higher-level pattern recognition and social network analysis. In addition to predicting attributes of individuals, we use the relational model to predict the outcome of an event, in this case, the fate of a hostage in a kidnapping event. Given a particular hostage taking event, the system will be able to predict the probability that the hostage will be released or killed based on known properties of the event. Features in the this model might include ransom demands and payment, regions and countries of the event, hostage nationality, and groups or individuals involved along with their past activities. Each of these features indicates the likelihood that a successful hostage release can be negotiated. The aggregration of relational features such as the percentage of hostages released by similar groups in the past can be used to improve performance. Aggregation 409 Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.
