Most of the existing studies about web spam detection explicitly or implicitly assume that the detection process is performed offline on the search engine side. However, we argue that online web spam detection is even useful in some specific scenarios. We propose to implement a web browser plug-in to support online web spam detection. Three different sets of spam labeling data are collected and adopted for learning a reliable web spam classifier. An empirical study is conducted on the benchmark web spam data collection. The statistical analysis of the data set verifies the necessity of online web spam detection. The performance of the proposed ensemble learning framework for online web spam detection is also examined and it meets the requirement of online webs Pam detection.

Most of the extant studies about web spam detection either explicitly or implicitly assume that web spam detection is performed on the server side of a search engine. In this paper, we argue that in some scenarios, web spam detection is preferred to be conducted on the client side (e.g., intelligent web browsers). When a page is viewed using an intelligent web browser, an integrated personalized web spam detector can determine whether the page is spam or not specifically tailored to the user's judgements. We propose a framework for implementing a personalized web spam detector. The experimental results obtained from an empirical evaluation confirmed the effectiveness of the proposed personalized web spam detection method.

Recent studies about web spam detection have utilized various content-based and link-based features to construct a spam classification model. In this paper, we conduct a thorough analysis of content spam on the web using topic models and propose several novel topical diversity measures for content spam detection. We adopt the web spam benchmark data set WEBSPAM-UK2007 for evaluation, and the experimental results verify that by integrating our topical diversity measures the performance of the state-of-the-art web spam detection methods can be greatly improved. In addition, comparing to existing features for training spam classification models, our topical diversity measures can achieve high spam detection performance using small set of training data. In personalized web spam detection, the training data (i.e., user's spam labeling results) are typically small. Our finding makes personalized web spam detection highly achievable. We develop an efficient and effective regression model using topical diversity measures for personalized web spam detection, and present some promising results obtained from an empirical study.

Given the proliferation of technology sites and the growing diversity of their readership, readers are more and more likely to encounter specialized language and terminology that they may lack the sufficient background to understand. Such sites may lose readership and the experience of readers may be impacted negatively if readers cannot quickly and easily find information about terms they wish to learn more about. We developed a system using a fusion of web log mining techniques that extracts, identifies, and recommends personalized terms to readers by utilizing information found in individual and global web query logs. In addition, the system presents relevant information related to these terms inline with the text. Our system outperforms some other related systems developed in the literature with special regard to usability.

Recently, privacy preserving data publishing has received a lot of attention in both research and applications. Most of the previous studies, however, focus on static data sets. In this paper, we study an emerging problem of continuous privacy preserving publishing of data streams which cannot be solved by any straightforward extensions of the existing privacy preserving publishing methods on static data. To tackle the problem, we develop a novel approach which considers both the distribution of the data entries to be published and the statistical distribution of the data stream. An extensive performance study using both real data sets and synthetic data sets verifies the effectiveness and the efficiency of our methods.

Keyword search has been recently extended to relational databases to retrieve information from text-rich attributes. However, all the existing methods focus on finding individual tuples matching a set of query keywords from one table or the join of multiple tables. In this paper, we motivate a novel problem of aggregate keyword search: finding minimal group-bys covering a set of query keywords well, which is useful in many applications. We develop two interesting approaches to tackle the problem, and further extend our methods to allow partial matches. An extensive empirical evaluation using both real data sets and synthetic data sets is reported to verify the effectiveness of aggregate keyword search and the efficiency of our methods.

Search logs, which contain rich and up-to-date information about users' needs and preferences, have become a critical data source for search engines. Recently, more and more data-driven applications are being developed in search engines based on search logs, such as query suggestion, keyword bidding, and dissatisfactory query analysis. In this paper, by observing that many data-driven applications in search engines highly rely on online mining of search logs, we develop an OLAP system on search logs which serves as an infrastructure supporting various data-driven applications. An empirical study using real data of over two billion query sessions demonstrates the usefulness and feasibility of our design.

Co-authorship networks, an important type of social networks, have been studied extensively from various angles such as degree distribution analysis, social community extraction and social entity ranking. Most of the previous studies consider the co-authorship relation between two authors as a collaboration. In this paper, we introduce a novel and interesting “supportiveness” measure on co-authorship relation. The fact that two authors co-author one paper can be regarded as one author supports the other's scientific work. We propose several supportiveness measures, and exploit a supportiveness-based author ranking scheme. Several efficient algorithms are developed to compute the top-n most supportive authors. Moreover, we extend the supportiveness analysis to community extraction, and develop feasible solutions to identify the most supportive groups of authors. The empirical study conducted on a large real data set indicates that the supportiveness measures are interesting and meaningful, and our methods are effective and efficient in practice.

Currently, most popular Web search engines adopt some link-based ranking methods such as PageRank. Driven by the huge potential benefit of improving rankings of Web pages, many tricks have been attempted to boost page rankings. The most common way, which is known as link spam, is to make up some artificially designed link structures. Detecting link spam effectively is a big challenge. In this article, we develop novel and effective detection methods for link spam target pages using page farms. The essential idea is intuitive: whether a page is the beneficiary of link spam is reflected by how it collects its PageRank score. Technically, how a target page collects its PageRank score is modeled by a page farm, which consists of pages contributing a major portion of the PageRank score of the target page. We propose two spamicity measures based on page farms. They can be used as an effective measure to check whether the pages are link spam target pages. An empirical study using a newly available real dataset strongly suggests that our method is effective. It outperforms the state-of-the-art methods like SpamRank and SpamMass in both precision and recall.

Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.

Web spam, which refers to any deliberate actions bringing to selected web pages an unjustifiable favorable relevance or importance, is one of the major obstacles for high quality information retrieval on the web. Most of the existing web spam detection methods are supervised that require a large and representative training set of web pages. Moreover, they often assume some global information such as a large web graph and snapshots of a large collection of web pages. However, in many situations such assumptions may not hold. In this paper, we study the problem of unsupervised web spam detection. We introduce the notion of spamicity to measure how likely a page is spam. Spamicity is a more flexible and user-controllable measure than the traditional supervised classification methods. We propose efficient online link spam and term spam detection methods using spamicity. Our methods do not need training and are cost effective. A real data set is used to evaluate the effectiveness and the efficiency of our methods.

With the expansion of the internet, many specialized, high-profile sites have become available that bring very technical subject matter to readers with non-technical backgrounds. While the theme of these sites may be of interest to these readers, the posts themselves may contain terms that non-experts may be unfamiliar with and may wish to know more about. We developed PLEDS, a personalized entity detection system which identifies interesting entities and provides related information for individual users by mining web logs and query logs. The experimental results of a systemic user study shows that with PLEDS's aid, users can experience the benefits of an enriched internet surfing experience.

The Web is a very large social network. It is important and interesting to understand the “ecology” of the Web: the general relations of Web pages to their environment. The understanding of such relations has a few important applications, including Web community identification and analysis, and Web spam detection. In this paper, we propose the notion of page farm, which is the set of pages contributing to (a major portion of) the PageRank score of a target page. We try to understand the “landscapes” of page farms in general: how are farms of Web pages similar to or different from each other? In order to sketch the landscapes of page farms, we need to extract page farms extensively. We show that computing page farms is NP-hard, and develop a simple greedy algorithm. Then, we analyze the farms of a large number of (over 3 million) pages randomly sampled from the Web, and report some interesting findings. Most importantly, the landscapes of page farms tend to also follow the power law distribution. Moreover, the landscapes of page farms strongly reflect the importance of the Web pages.