How Question Answering Technology Helps to Locate Malevolent Online Content

  • Dmitri Roussinov
  • Jose Antonio Robles-Flores
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3495)


The inherent lack of control over the Internet content resulted in proliferation of online material that can be potentially detrimental. For example, the infamous “Anarchist Cookbook” teaching how to make weapons, home made bombs, and poisons, keeps re-appearing in various places. Some websites teach how to break into computer networks to steal passwords and credit card information. Law enforcement, security experts, and public watchdogs started to locate, monitor, and act when such malevolent content surfaces on the Internet. Since the resources of law enforcement are limited, it may take some time before potentially malevolent content is located, enough for it to disseminate and cause harm. The only practical way for searching the content of the Internet, available for law enforcement, security experts, and public watchdogs is by using a search engine, such as Google, AOL, MSN, etc. We have suggested and empirically evaluated an alternative technology (automated question answering or QA) capable of locating potentially malevolent online content. We have implemented a proof-of-concept prototype that is capable of finding web pages that provide the answers to given questions (e.g. “How to build a pipe bomb?”). Using students as subjects in a controlled experiment, we have empirically established that our QA prototype finds web pages that are more likely to provide answers to given questions than simple keyword search using Google. This suggests that QA technology can be a good replacement or an addition to the traditional keyword searching for the task of locating malevolent online content and, possibly, for a more general task of interactive online information exploration.


Question Answering Software Piracy Security Expert Question Answering System Reciprocal Rank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dmitri Roussinov
    • 1
  • Jose Antonio Robles-Flores
    • 1
    • 2
  1. 1.Dept of Information Systems, W.P. Carey School of BusinessArizona State UniversityTempeUSA
  2. 2.ESAN UniversityLimaPeru

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