The Experts below are selected from a list of 924 Experts worldwide ranked by ideXlab platform
Mohammadreza Ebrahimi - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning
2016Co-Authors: Mohammadreza EbrahimiAbstract:Providing a safe environment for juveniles and children in Online social networks is considered as a major factor in improving public safety. Due to the prevalence of the Online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of Online Predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Surete du Quebec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Surete du Quebec.
Ebrahimi Mohammadreza - One of the best experts on this subject based on the ideXlab platform.
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Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning
2016Co-Authors: Ebrahimi MohammadrezaAbstract:Providing a safe environment for juveniles and children in Online social networks is considered as a major factor in improving public safety. Due to the prevalence of the Online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of Online Predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Sûreté du Québec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Sûreté du Québec
Silvia Cristina Bettez - One of the best experts on this subject based on the ideXlab platform.
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Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual
2016Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement
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ICIS - Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual Predators
2014Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement.
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Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual Predators Completed Research Paper
2014Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement.
Connie S. Barber - One of the best experts on this subject based on the ideXlab platform.
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Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual
2016Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement
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ICIS - Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual Predators
2014Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement.
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Deconstructing the Online Grooming of Youth: Toward Improved Information Systems for Detection of Online Sexual Predators Completed Research Paper
2014Co-Authors: Connie S. Barber, Silvia Cristina BettezAbstract:The aggressive Online solicitation of youth by Online sexual Predators has been established as an unintended consequence of the connectedness afforded individuals through social media. Computer science research that has focused on the detection of Online sexual Predators is scant and absent behavioral theory. We address this gap through examining what behavioral patterns emerge regarding how Online sexual Predators use language inside of social media to groom youth. Through a grounded theory analysis of ninety Perverted Justice (PVJ) transcripts, of conversations between convicted Online sexual Predators and PVJ volunteers who posed as youth, we identified five categories of Online Predator behavior inside of text during victimization of children. Those categories are: assessment, enticements, cybersexploitation, control and self-preservation. The aim of the research is twofold: (a) to improve pattern recognition programming for automated detection software, and (b) to improve educational tools for youth, parents, guardians, educators, and law enforcement.
Grover Jayna - One of the best experts on this subject based on the ideXlab platform.
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Online Predators
Southern New Hampshire University, 2015Co-Authors: Grover JaynaAbstract:This project explores how the emergence of social media negatively affects the youth of our nation by putting them at risk for many Online dangers, most importantly Online sexual Predators. Children who use social media have no regard for Online safety, which puts them at an increased risk for falling victim to an Online Predator. Additionally, parents, teachers and the general public lack knowledge on Online safety that is needed to inform and protect our youth from Online Predators and many other Online dangers, and this lack of knowledge is a crucial piece in preventing Online Predators from targeting children who carelessly use social media. Online Predators use the grooming process to target and seduce their victims. Once a child falls victim, the perpetrator can easily convince them to have sexual relations that are both harmful and illegal. The public continues to do nothing to make children, parents and educators aware of the current threat Online Predators pose. This research project examines the legal aspect on the conviction of Online Predators, along with parental and public solutions to limit the amount of Predators roaming the Internet. As a society it is our duty to protect the youth from such dangers. At age eleven kids should be out playing with their friends not being sexually and mentally abused by Online Predators. If society as a whole does not try to prevent this issue it is only going to become more of a threat as technology and social media progress. By having longer conviction time, more legal resources to track Predators, increased public awareness and stricter parental involvement Online Predators can finally be stopped. (Author abstract)Grover, J. (2015). Online Predators. Retrieved from http://academicarchive.snhu.ed
