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.
FriendFeed has recently launched a search feature, and so Facebook search must be coming soon. Real-time Web search (of streams of activities) is a hot topic right now. ...
R. Kumar, A. Tomkins, S. Vassilvitskii, и E. Vee. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, стр. 359--368. ACM, (2015)
M. Bilal, и S. Kang. International Journal of Communication Systems, 30 (5):
e3309(2017)cite arxiv:1702.04055Comment: This article is accepted for the publication in "International Journal of Communication Systems".