How Question Answering Technology Helps to Locate Malevolent Online Content

The inherent lack of control over the Internet content resulted in proliferation of online material that can be potentially detrimental. For example, the infamous “Anarchist Cookbook” teaching how to make weapons, home made bombs, and poisons, keeps re-appearing in various places. Some websites teach how to break into computer networks to steal passwords and credit card information. Law enforcement, security experts, and public watchdogs started to locate, monitor, and act when such malevolent content surfaces on the Internet. Since the resources of law enforcement are limited, it may take some time before potentially malevolent content is located, enough for it to disseminate and cause harm. Currently applied approach for searching the content of the Internet, available for law enforcement and public watchdogs is by using a search engine, such as Google, AOL, MSN, etc. We have suggested and empirically evaluated an alternative technology (called automated question answering or QA) capable of locating potentially malevolent online content. We have implemented a proof-of-concept prototype that is capable of finding web pages that may potentially contain the answers to specified questions (e.g. “How to steal a password?”). Using students as subjects in a controlled experiment, we have empirically established that our QA prototype finds web pages that are more likely to provide answers to given questions than simple keyword search using Google. This suggests that QA technology can be a good replacement or an addition to the traditional keyword searching for the task of locating malevolent online content and, possibly, for a more general task of interactive online information exploration

Bubble World - A Novel Visual Information Retrieval Technique

With the tremendous growth of published electronic information sources in the last decade and the unprecedented reliance on this information to succeed in day-to-day operations, comes the expectation of finding the right information at the right time. Sentential interfaces are currently the only viable solution for searching through large infospheres of unstructured information, however, the simplistic nature of their interaction model and lack of cognitive amplification they can provide severely limit the performance of the interface. Visual information retrieval systems are emerging as possible candidate replacements for the more traditional interfaces, but many lack the cognitive framework to support the knowledge crystallization process found to be essential in information retrieval. This work introduces a novel visual information retrieval technique crafted from two distinct design genres: (1) the cognitive strategies of the human mind to solve problems and (2) observed interaction patterns with existing information retrieval systems. Based on the cognitive and interaction framework developed in this research, a functional prototype information retrieval system, called Bubble World, has been created to demonstrate that significant performance gains can be achieved using this technique when compared to more traditional text-based interfaces. Bubble World does this by successfully transforming the internal mental representation of the information retrieval problem to an efficient external view, and then through visual cues, provides cognitive amplification at key stages of the information retrieval process. Additionally, Bubble World provides the interaction model and the mechanisms to incorporate complex search schemas into the retrieval process either manually or automatically through the use of predefined ontological models

Study of result presentation and interaction for aggregated search

The World Wide Web has always attracted researchers and commercial search engine companies due to the enormous amount of information available on it. "Searching" on web has become an integral part of today's world, and many people rely on it when looking for information. The amount and the diversity of information available on the Web has also increased dramatically. Due to which, the researchers and the search engine companies are making constant efforts in order to make this information accessible to the people effectively. Not only there is an increase in the amount and diversity of information available online, users are now often seeking information on broader topics. Users seeking information on broad topics, gather information from various information sources (e.g, image, video, news, blog, etc). For such information requests, not only web results but results from different document genre and multimedia contents are also becoming relevant. For instance, users' looking for information on "Glasgow" might be interested in web results about Glasgow, Map of Glasgow, Images of Glasgow, News of Glasgow, and so on. Aggregated search aims to provide access to this diverse information in a unified manner by aggregating results from different information sources on a single result page. Hence making information gathering process easier for broad topics. This thesis aims to explore the aggregated search from the users' perspective. The thesis first and foremost focuses on understanding and describing the phenomena related to the users' search process in the context of the aggregated search. The goal is to participate in building theories and in understanding constraints, as well as providing insights into the interface design space. In building this understanding, the thesis focuses on the click-behavior, information need, source relevance, dynamics of search intents. The understanding comes partly from conducting users studies and, from analyzing search engine log data. While the thematic (or topical) relevance of documents is important, this thesis argues that the "source type" (source-orientation) may also be an important dimension in the relevance space for investigating in aggregated search. Therefore, relevance is multi-dimensional (topical and source-orientated) within the context of aggregated search. Results from the study suggest that the effect of the source-orientation was a significant factor in an aggregated search scenario. Hence adds another dimension to the relevance space within the aggregated search scenario. The thesis further presents an effective method which combines rule base and machine learning techniques to identify source-orientation behind a user query. Furthermore, after analyzing log-data from a search engine company and conducting user study experiments, several design issues that may arise with respect to the aggregated search interface are identified. In order to address these issues, suitable design guidelines that can be beneficial from the interface perspective are also suggested. To conclude, aim of this thesis is to explore the emerging aggregated search from users' perspective, since it is a very important for front-end technologies. An additional goal is to provide empirical evidence for influence of aggregated search on users searching behavior, and identify some of the key challenges of aggregated search. During this work several aspects of aggregated search will be uncovered. Furthermore, this thesis will provide a foundations for future research in aggregated search and will highlight the potential research directions

Visual Interactions with a Multidimensional Ranked List

Performance analysis of an interactive visualization system generally requires an extensive user study, a method that is very expensive and that often yields inconclusive results. To do a successful user study, the researcher has to be well aware of the system's possibilities. In this paper we present a different kind of analysis. We show how the system behavior and performance could be investigated off-line, without direct user intervention. We introduce an evaluation measure to assess the quality of a multidimensional visualization. Next, we suggest two methods for dealing with user's feedback. We also discuss the effect the dimensionality of the visualization has on the actual performance. 1 Introduction An information retrieval system places retrieved documents in a list in the order they are most likely to be relevant: the first document is the best match to the user's query, the second is the next most likely to be helpful, and so on. We are interested in situations where..