Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically

By Steve Webb, James Caverlee, Calton Pu. 2006

127 citations recorded by the source corpus.

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Citations in the corpus

Email Spam Filtering: A Systematic Review (2008, background): “It applies also to web spam, which uses a search engine as its delivery mechanism [187, 188].”
A second-order method for strongly convex ℓ1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document}-re (2013, unknown): “00e−00 [29] webspam 350,000 16,609,143 2.”
Deep Learning for Anomaly Detection (2020, background): “D Cup 14 619,326 10 6.00% Tabular [115,116] Dropout prediction KDD Cup 15 35,091 27 0.10%-0.40% Sequence [93] Malicious URLs detection URL [95] 2.4m 3.2m 33.04% Streaming [114] Spam detection Webspam [161] 350,000 16.6m 39.61% Tabular/text [114] Fraud detection Credit-card-fraud [34] 284,807 30 0.17”
SparCML: High-Performance Sparse Communication for Machine Learning (2018, unknown): “The immediate system implication, which we exploit in S PAR CML, is that the updates which nodes wish to communicate are either naturally sparse [49], or can be sparsiﬁed in a principled manner , without loss of convergence [2, 3, 13,44].”
Adversarial Web Search (2011, background): “The Webb Spam Corpus3 [233] is a collection of 350,000 Web spam pages.”
Distributed Optimization for Non-Strongly Convex Regularizers (2016, methodology): “• webspam[16]: The webspam dataset is a binary classification dataset where every data point is constructed by taking trigrams of a document and it has a spam/non-spam label.”
Spamming botnets: signatures and characteristics (2008, methodology): “Webb et al. studied the link between spam emails and Web spam using a large Web spam corpus [ 23 ].”