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Dark Web pp 295-318 | Cite as

Extremist YouTube Videos

  • Hsinchun Chen
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 30)

Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information, such as personal preferences and interests, and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing web sites. In this chapter, we proposed a text-based framework for video content classification of online video-sharing web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and SVM) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users’ interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. SVM outperformed C4.5 and Naïve Bayes in our experiments. In addition, our case study further demonstrated that accurate video classification results are very useful for identifying implicit cyber communities on video-sharing web sites.

Keywords

Support Vector Machine Text Feature Gaussian Mixture Model Semantic Concept Online Video 
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.

Notes

Acknowledgments

This work was supported by the NSF Computer and Network Systems (CNS) Program, “(CRI: CRD) Developing a Dark Web Collection and Infrastructure for Computational and Social Sciences” (CNS-0709338), September 2007–August 2010.

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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

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