Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Toward Spotting the Pedophile Telling victim from predator in text chats
ICSC '07 Proceedings of the International Conference on Semantic Computing
Supporting Law Enforcement in Digital Communities through Natural Language Analysis
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Topic Detection and Extraction in Chat
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Sentiment analysis for effective detection of cyber bullying
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Governance within social media websites: Ruling new frontiers
Telecommunications Policy
Modelling fixated discourse in chats with cyberpedophiles
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
On the impact of sentiment and emotion based features in detecting online sexual predators
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Towards detection of child sexual abuse media: categorization of the associated filenames
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Detecting cyberbullying: query terms and techniques
Proceedings of the 5th Annual ACM Web Science Conference
Exploring high-level features for detecting cyberpedophilia
Computer Speech and Language
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This work integrates communication theories and computer science algorithms to create a program that can detect the occurrence of sexual predation in an online social setting. Although much work has discussed social media in general, this particular aspect of online social interaction remains largely unexplored. In previous work we developed phrase-matching and rule-based approaches to classify and label lines of chat logs. In the current work we expand these techniques and use machine learning algorithms to classify posts. Our machine learning system leveraged the phrase-matching and rule-based systems to identify appropriate attributes for our supervised learning algorithms. Our machine learning experiments confirmed that the rules we developed are adequate to identify the coding rules. Neither decision trees nor instance-based learning algorithms were able to significantly improve upon the 68 percent accuracy we were able to achieve using the rule-based methods employed by a software program called ChatCoder 2, as described here.