Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. As social networks like Facebook, Twitter and Google+ have become important communication platforms in people's daily lives, malicious users make them as major targets to pollute with low quality information, which we also call as Denial of Information (DoI) attacks. How to analyze and detect low quality information in social networks for preventing DoI attacks is the major research problem I will address in this dissertation. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My work contains three major parts: 1). analytics and detection framework of low quality information, 2). evolutionary study of low quality information, and 3). detection approaches of low quality information. In part I, I proposed social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. In part II, I performed a large-scale evolutionary study on web page spam and email spam over a long period of time. In part III, I designed three detection approaches used in detecting low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumors in social networks. Our study shows promising results in analyzing and detecting low quality information in social networks.

The advent of the Internet has generated a proliferation of online information-rich environments, which provide information consumers with an unprecedented amount of freely available information. However, the openness of these environments has also made them vulnerable to a new class of attacks called Denial of Information (DoI) attacks. Attackers launch these attacks by deliberately inserting low quality information into information-rich environments to promote that information or to deny access to high quality information. These attacks directly threaten the usefulness and dependability of online information-rich environments, and as a result, an important research question is how to automatically identify and remove this low quality information from these environments. The first contribution of this thesis research is a set of techniques for automatically recognizing and countering various forms of DoI attacks in email systems. We develop a new DoI attack based on camouflaged messages, and we show that spam producers and information consumers are entrenched in a spam arms race. To break free of this arms race, we propose two solutions. One solution involves refining the statistical learning process by associating disproportionate weights to spam and legitimate features, and the other solution leverages the existence of non-textual email features (e.g., URLs) to make the classification process more resilient against attacks. The second contribution of this thesis is a framework for collecting, analyzing, and classifying examples of DoI attacks in the World Wide Web. We propose a fully automatic Web spam collection technique and use it to create the Webb Spam Corpus -- a first-of-its-kind, large-scale, and publicly available Web spam data set. Then, we perform the first large-scale characterization of Web spam using content and HTTP session analysis. Next, we present a lightweight, predictive approach to Web spam classification that relies exclusively on HTTP session information. The final contribution of this thesis research is a collection of techniques that detect and help prevent DoI attacks within social environments. First, we provide detailed descriptions for each of these attacks. Then, we propose a novel technique for capturing examples of social spam, and we use our collected data to perform the first characterization of social spammers and their behaviors.