Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such non-direct work and describes the development of tools to characterize conflict and coordination costs in Wikipedia. The results may inform the design of new collaborative knowledge systems.Second, we build a characterization model for conflict at the article level. Using human-labeled controversy tags as ground truth, we show that a machine learner has high CHI

Wikipedia is a wiki-based encyclopedia that has become one of the most popular collaborative on-line knowledge systems. As in any large collaborative system, as Wikipedia has grown, conflicts and coordination costs have increased dramatically. Visual analytic tools provide a mechanism for addressing these issues by enabling users to more quickly and effectively make sense of the status of a collaborative environment. In this paper we describe a model for identifying patterns of conflicts in Wikipedia articles. The model relies on users' editing history and the relationships between user edits, especially revisions that void previous edits, known as "reverts". Based on this model, we constructed Revert Graph, a tool that visualizes the overall conflict patterns between groups of users. It enables visual analysis of opinion groups and rapid interactive exploration of those relationships via detail drilldowns. We present user patterns and case studies that show the effectiveness of these techniques, and discuss how they could generalize to other systems.

Intelligence analysis often involves the task of gathering information about an organization. Knowledge about individuals in an organization and their relationships, often represented as a hierarchical organization chart, is crucial for understanding the organization. However, it is difficult for intelligence analysts to follow all individuals in an organization. Existing hierarchy visualizations have largely focused on the visualization of fixed structures and can not effectively depict the evolution of a hierarchy over time. We introduce TimeTree, a novel visualization tool designed to enable exploration of a changing hierarchy. TimeTree enables analysts to navigate the history of an organization, identify events associated with a specific entity (visualized on a TimeSlider), and explore an aggregate view of an individual's career path (a CareerTree). We demonstrate the utility of TimeTree by investigating a set of scenarios developed by an expert intelligence analyst. The scenarios are evaluated using a real dataset composed of eighteen thousand career events from more than eight thousand individuals. Insights gained from this analysis are presented.

Abstract. Phishing attacks are a significant threat to users of the Internet, causing tremendous economic loss every year. In combating phish, industry relies heavily on manual verification to achieve a low false positive rate, which, however, tends to be slow in responding to the huge volume of unique phishing URLs created by toolkits. Our goal here is to combine the best aspects of human verified blacklists and heuristic-based methods, i.e., the low false positive rate of the former and the broad coverage of the latter. To that end, we present the design and evaluation of a hierarchical blacklist-enhanced phish detection framework. The key insight behind our detection algorithm is to leverage existing humanverified blacklists and apply the shingling technique, a popular nearduplicate detection algorithm used by search engines, to detect phish in a probabilistic fashion with very high accuracy. To achieve an extremely low false positive rate, we use a filtering module in our layered system, harnessing the power of search engines via information retrieval techniques to correct false positives. Comprehensive experiments over a diverse spectrum of data sources show that our method achieves 0% false positive rate (FP) with a true positive rate (TP) of 67.74% using searchoriented filtering, and 0.03% FP and 73.53% TP without the filtering module. With incremental model building capability via a sliding window mechanism, our approach is able to adapt quickly to new phishing variants, and is thus more responsive to the evolving attacks.

Phishing attacks are a significant security threat to users of the Internet, causing tremendous economic loss every year. Past work in academia has not been adopted by industry in part due to concerns about liability over false positives. However, blacklist-based methods heavily used in industry are slow in responding to new phish attacks, and tend to be easily overwhelmed by phishing techniques such as fast-flux and the proliferation of toolkits. In this paper, we present the design and evaluation of two blacklist-enhanced content-based algorithms. The key insight behind our algorithms is to leverage existing human-verified whitelists and blacklists, and relax them via probabilistic methods to attain high true positive rates while maintaining extremely low false positive rates. Comprehensive experiments over a diverse spectrum of data sources show that our approach currently achieves a false positive rate of 0.0434% with a true positive rate of 87.42%. Our algorithms are able to adapt quickly to new phishing attacks by incremental retraining, and present a new framework that will generalize to evolving attacks.