Results 1 - 10
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231
Detecting Spam Web Pages through Content Analysis
- In Proceedings of international conference on World Wide Web 2006 Myle Ott, Yejin Choi, Claire Cardie
"... In this paper, we continue our investigations of “web spam”: the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatica ..."
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Cited by 207 (4 self)
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In this paper, we continue our investigations of “web spam”: the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatically detecting spam pages, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms. When combined, our heuristics correctly identify 2,037 (86.2%) of the 2,364 spam pages (13.8%) in our judged collection of 17,168 pages, while misidentifying 526 spam and non-spam pages (3.1%).
Opinion spam and analysis
- In Proceedings of the International Conference on Web Search and Web Data Mining (WSDM
, 2008
"... Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarizat ..."
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Cited by 160 (19 self)
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Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them.
Know your neighbors: Web spam detection using the web topology
- In Proceedings of SIGIR
, 2007
"... Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link ..."
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Cited by 108 (9 self)
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Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link dependencies among the Web pages, and the content of the pages themselves. We find that linked hosts tend to belong to the same class: either both are spam or both are non-spam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam that can be applied in practice to large-scale Web data.
Identifying link farm spam pages
- In Proc. of the 14th International WWW conference
, 2005
"... With the increasing importance of search in guiding today's web trac, more and more eort has been spent to cre-ate search engine spam. Since link analysis is one of the most important factors in current commercial search en-gines ' ranking systems, new kinds of spam aiming at links have ap ..."
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Cited by 105 (11 self)
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With the increasing importance of search in guiding today's web trac, more and more eort has been spent to cre-ate search engine spam. Since link analysis is one of the most important factors in current commercial search en-gines ' ranking systems, new kinds of spam aiming at links have appeared. Building link farms is one technique that can deteriorate link-based ranking algorithms. In this paper, we present algorithms for detecting these link farms automati-cally by rst generating a seed set based on the common link set between incoming and outgoing links of Web pages and then expanding it. Links between identied pages are re-weighted, providing a modied web graph to use in ranking page importance. Experimental results show that we can identify most link farm spam pages and the nal ranking results are improved for almost all tested queries.
SpamRank -- Fully Automatic Link Spam Detection
- IN PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON ADVERSARIAL INFORMATION RETRIEVAL ON THE WEB (AIRWEB
, 2005
"... Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists ..."
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Cited by 96 (5 self)
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Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed pages have a biased distribution of pages that contribute to the undeserved high PageRank value. We define SpamRank by penalizing pages that originate a suspicious PageRank share and personalizing PageRank on the penalties. Our method is tested on a 31 M page crawl of the .de domain with a manually classified 1000-page stratified random sample with bias towards large PageRank values.
A reference collection for Web spam
- SIGIR Forum
, 2006
"... We describe the WEBSPAM-UK2006 collection, a large set of Web pages that have been manually annotated with labels indicating if the hosts are include Web spam aspects or not. This is the first publicly available Web spam collection that includes page contents and links, and that has been labelled by ..."
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Cited by 73 (13 self)
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We describe the WEBSPAM-UK2006 collection, a large set of Web pages that have been manually annotated with labels indicating if the hosts are include Web spam aspects or not. This is the first publicly available Web spam collection that includes page contents and links, and that has been labelled by a large and diverse set of judges. 1
SVMs for the Blogosphere: Blog identification and Splog detection
- In Proc. 2006 AAAI Spring Symp. Computational Approaches to Analyzing Weblogs
, 2006
"... Weblogs, or blogs have become an important new way to publish information, engage in discussions and form communities. The increasing popularity of blogs has given rise to search and analysis engines focusing on the “blogosphere”. A key requirement of such systems is to identify blogs as they crawl ..."
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Cited by 72 (7 self)
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Weblogs, or blogs have become an important new way to publish information, engage in discussions and form communities. The increasing popularity of blogs has given rise to search and analysis engines focusing on the “blogosphere”. A key requirement of such systems is to identify blogs as they crawl the Web. While this ensures that only blogs are indexed, blog search engines are also often overwhelmed by spam blogs (splogs). Splogs not only incur computational overheads but also reduce user satisfaction. In this paper we first describe experimental results of blog identification using Support Vector Ma-chines (SVM). We compare results of using different feature sets and introduce new features for blog iden-tification. We then report preliminary results on splog detection and identify future work.
Link spam alliances
- In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB
, 2005
"... Link spam is used to increase the ranking of certain target web pages by misleading the connectivity-based ranking algorithms in search engines. In this paper we study how web pages can be interconnected in a spam farm in order to optimize rankings. We also study alliances, that is, interconnections ..."
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Cited by 65 (1 self)
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Link spam is used to increase the ranking of certain target web pages by misleading the connectivity-based ranking algorithms in search engines. In this paper we study how web pages can be interconnected in a spam farm in order to optimize rankings. We also study alliances, that is, interconnections of spam farms. Our results identify the optimal structures and quantify the potential gains. In particular, we show that alliances can be synergistic and improve the rankings of all participants. We believe that the insights we gain will be useful in identifying and combating link spam. 1
