Web spam pages use various techniques to achieve
higher-than-deserved rankings in a search engine’s
results. While human experts can identify
spam, it is too expensive to manually evaluate a
large number of pages. Instead, we propose techniques
to semi-automatically separate reputable,
good pages from spam. We first select a small set
of seed pages to be evaluated by an expert. Once
we manually identify the reputable seed pages, we
use the link structure of the web to discover other
pages that are likely to be good. In this paper
we discuss possible ways to implement the seed
selection and the discovery of good pages. We
present results of experiments run on the World
Wide Web indexed by AltaVista and evaluate the
performance of our techniques. Our results show
that we can effectively filter out spam from a significant
fraction of the web, based on a good seed
set of less than 200 sites.
D. Donato, F. Bonchi, T. Chi, und Y. Maarek. Proceedings of the 19th international conference on World wide web, Seite 321--330. New York, NY, USA, ACM, (2010)
B. Yu, und M. Singh. Cooperative Information Agents IV - The Future of Information Agents in Cyberspace, Volume 1860 von Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, (2000)
B. Yu, und M. Singh. Proceedings of the second international joint conference on Autonomous agents and multiagent systems, Seite 65--72. New York, NY, USA, ACM, (2003)
P. Jurczyk, und E. Agichtein. Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, Seite 919--922. New York, NY, USA, ACM, (2007)
M. Burghardt, M. Heckner, und C. Wolff. Volume 4 von Library and Information Science, Kapitel 2, Seite 19--46. Emerald Group Publishing Limited, (2012)
M. Morris. Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, Seite 1657--1660. New York, NY, USA, ACM, (2008)
K. McNally, M. O'Mahony, B. Smyth, M. Coyle, und P. Briggs. Proceedings of the 15th international conference on Intelligent user interfaces, Seite 179--188. New York, NY, USA, ACM, (2010)
B. Smyth, P. Briggs, M. Coyle, und M. O’Mahony. User Modeling, Adaptation, and Personalization, Volume 5535 von Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, (2009)