Detecting Misleading Information

"The problem of detecting intentionally misleading information in open sources requires accurate classification of the consistency of that information. Consistency can be measured internally to the information as well as by reference to backgroundknowledge. IET proposes a combination of natural language processing techniques and Bayesian classification to identify the fundamental characteristics of consistency for a selected domain. Assertions will be extracted from each piece of sourceinformation. These assertions will be compared to each other to assess their mutual consistency. Then, queries which can be issued against open sources of background knowledge will be created for each assertion. These queries will be run, and theirresults analyzed to provide external consistency measures. The combinations of assertions, queries and answers will then be used to construct a Bayesian Network which will be trained to correctly classify the source information with respect to misleadingcontent. The nodes of this network will identify the characteristics that will reliably predict consistency. The proliferation of open sources and the Internet for every commercial market provides conveniences for consumers as well as opportunities forcriminals. It also provides unprecedented opportunities for law enforcement, regulatory and other oversight agencies to review, analyze, identify, and prosecute such fraudulent behavior. Examples of mark