Advice for Web designers often includes an admonition to use short, scannable, bullet-pointed text, reflecting the common belief that browsing the Web most often involves scanning rather than reading. Literature from several disciplines focuses on the myriad combinations of factors related to online reading but studies of the users' interests and motivations appear to offer a more promising avenue for understanding how users utilize information on Web pages. This study utilized the modified Personal Involvement Inventory (PII), a ten-item instrument used primarily in the marketing and advertising fields, to measure interest and motivation toward a topic presented on the Web. Two sites were constructed from Reader's Digest Association, Inc. online articles and a program written to track students' use of the site. Behavior was measured by the initial choice of short versus longer versions of the main page, the number of pages visited and the amount of time spent on the site. Data were gathered from students at a small, private university in the southwest part of the United States to answer six hypotheses which posited that subjects with higher involvement in a topic presented on the Web and a more positive attitude toward the Web would tend to select the longer text version, visit more pages, and spend more time on the site. While attitude toward the Web did not correlate significantly with any of the behavioral factors, the level of involvement was associated with the use of the sites in two of three hypotheses, but only partially in the manner hypothesized. Increased involvement with a Web topic did correlate with the choice of a longer, more detailed initial Web page, but was inversely related to the number of pages viewed so that the higher the involvement, the fewer pages visited. An additional indicator of usage, the average amount of time spent on each page, was measured and revealed that more involved users spent more time on each page.

Millions of users share their experiences, thoughts, and interests online, through social media sites (e.g., Twitter, Flickr, YouTube). As a result, these sites host a substantial number of user-contributed documents (e.g., textual messages, photographs, videos) for a wide variety of events (e.g., concerts, political demonstrations, earthquakes). In this dissertation, we present techniques for leveraging the wealth of available social media documents to identify and characterize events of different types and scale. By automatically identifying and characterizing events and their associated user-contributed social media documents, we can ultimately offer substantial improvements in browsing and search quality for event content. To understand the types of events that exist in social media, we first characterize a large set of events using their associated social media documents. Specifically, we develop a taxonomy of events in social media, identify important dimensions along which they can be categorized, and determine the key distinguishing features that can be derived from their associated documents. We quantitatively examine the computed features for different categories of events, and establish that significant differences can be detected across categories. Importantly, we observe differences between events and other non-event content that exists in social media. We use these observations to inform our event identification techniques. To identify events in social media, we follow two possible scenarios. In one scenario, we do not have any information about the events that are reflected in the data. In this scenario, we use an online clustering framework to identify these unknown events and their associated social media documents. To distinguish between event and non-event content, we develop event classification techniques that rely on a rich family of aggregate cluster statistics, including temporal, social, topical, and platform-centric characteristics. In addition, to tailor the clustering framework to the social media domain, we develop similarity metric learning techniques for social media documents, exploiting the variety of document context features, both textual and non-textual. In our alternative event identification scenario, the events of interest are known, through user-contributed event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events). In this scenario, we can identify social media documents for the known events by exploiting known event features, such as the event title, venue, and time. While this event information is generally helpful and easy to collect, it is often noisy and ambiguous. To address this challenge, we develop query formulation strategies for retrieving event content on different social media sites. Specifically, we propose a two-step query formulation approach, with a first step that uses highly specific queries aimed at achieving high-precision results, and a second step that builds on these high-precision results, using term extraction and frequency analysis, with the goal of improving recall. Importantly, we demonstrate how event-related documents from one social media site can be used to enhance the identification of documents for the event on another social media site, thus contributing to the diversity of information that we identify. The number of social media documents that our techniques identify for each event is potentially large. To avoid overwhelming users with unmanageable volumes of event information, we design techniques for selecting a subset of documents from the total number of documents that we identify for each event. Specifically, we aim to select high-quality, relevant documents that reflect useful event information. For this content selection task, we experiment with several centrality-based techniques that consider the similarity of each event-related document to the central theme of its associated event and to other social media documents that correspond to the same event. We then evaluate both the relative and overall user satisfaction with the selected social media documents for each event. The existing tools to find and organize social media event content are extremely limited. This dissertation presents robust ways to organize and filter this noisy but powerful event information. With our event identification, characterization, and content selection techniques, we provide new opportunities for exploring and interacting with a diverse set of social media documents that reflect timely and revealing event content. Overall, the work presented in this dissertation provides an essential methodology for organizing social media documents that reflect event information, towards improved browsing and search for social media event data.

Chan Ka Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves i-iv (3rd gp.)). / Abstracts in English and Chinese. / ACKNOWLEDGEMENT --- p.I / ABSTRACT --- p.II / 摘要 --- p.IV / TABLE OF CONTENT --- p.VI / LIST OF FIGURE --- p.VIII / LIST OF TABLE --- p.IX / Chapter CHAPTER 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Importance of hyperlink analysis --- p.2 / Chapter CHAPTER 2. --- RELATED WORK --- p.4 / Chapter 2.1 --- Crawling --- p.4 / Chapter 2.1.1 --- Crawling method for HITS Algorithm --- p.4 / Chapter 2.1.2 --- Crawling method for Page Rank Algorithm --- p.7 / Chapter 2.2 --- Ranking --- p.7 / Chapter 2.2.1 --- Page Rank Algorithm --- p.8 / Chapter 2.2.2 --- HITS Algorithm --- p.11 / Chapter 2.2.3 --- PageRank-HITS Algorithm --- p.15 / Chapter 2.2.4 --- SALSA Algorithm --- p.16 / Chapter 2.2.5 --- Average and Sim --- p.18 / Chapter 2.2.6 --- Netscape Approach --- p.19 / Chapter 2.2.7 --- Cocitation Approach --- p.19 / Chapter 2.3 --- Multimedia Information Retrieval --- p.20 / Chapter 2.3.1 --- Octopus --- p.21 / Chapter CHAPTER 3. --- RESEARCH METHODOLOGY --- p.25 / Chapter 3.1 --- Research Objective --- p.25 / Chapter 3.2 --- Proposed Crawling Methodology --- p.26 / Chapter 3.2.1 --- Collecting Media Objects --- p.26 / Chapter 3.2.2 --- Filtering the collection of links --- p.29 / Chapter 3.3 --- Proposed Ranking Methodology --- p.34 / Chapter 3.3.1 --- Identifying the factors affect ranking --- p.34 / Chapter 3.3.2 --- Modified Ranking Algorithms --- p.37 / Chapter CHAPTER 4. --- EXPERIMENTAL RESULTS AND DISCUSSIONS --- p.52 / Chapter 4.1 --- Experimental Setup --- p.52 / Chapter 4.1.1 --- Assumptions for the Experiment --- p.53 / Chapter 4.2 --- Some Observations from Experiment --- p.54 / Chapter 4.2.1 --- Dangling links --- p.55 / Chapter 4.2.2 --- "Good Hub = bad Authority, Good Authority = bad Hub?" --- p.55 / Chapter 4.2.3 --- Setting of weights --- p.56 / Chapter 4.3 --- Discussion on Experimental Results --- p.57 / Chapter 4.3.1 --- Relevance --- p.57 / Chapter 4.3.2 --- Precision and recall --- p.58 / Chapter 4.3.3 --- Significance testing --- p.61 / Chapter 4.3.4 --- Ranking --- p.63 / Chapter 4.4 --- Limitations and Difficulties --- p.67 / Chapter 4.4.1 --- Small size of the base set --- p.68 / Chapter 4.4.2 --- Parameter settings --- p.68 / Chapter 4.4.3 --- Unable to remove all the meaningless links from base set --- p.68 / Chapter 4.4.4 --- Resources and time-consuming --- p.69 / Chapter 4.4.5 --- TKC Effect --- p.69 / Chapter 4.4.6 --- Continuously updated format of HTML codes and file types --- p.70 / Chapter 4.4.7 --- The object citation habit of authors --- p.70 / Chapter CHAPTER 5. --- CONCLUSION --- p.71 / Chapter 5.1 --- Contribution of our Methodology --- p.71 / Chapter 5.2 --- Possible Improvement --- p.71 / Chapter 5.3 --- Conclusion --- p.72 / BIBLIOGRAPHY --- p.I / APPENDIX --- p.A-I / Chapter A.1 --- One-tailed paired t-test results --- p.A-I / Chapter A2. --- Anova results --- p.A-IV

Liu, Ke. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 41-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Keyword Expansion --- p.7 / Chapter 2.2 --- Content-based Image Search and Visual Expansion --- p.8 / Chapter 3 --- Algorithm --- p.12 / Chapter 3.1 --- Overview --- p.12 / Chapter 3.2 --- Visual Distance Calculation --- p.14 / Chapter 3.2.1 --- Visual Features --- p.15 / Chapter 3.2.2 --- Adaptive Weight Schema --- p.17 / Chapter 3.3 --- Keyword Expansion --- p.18 / Chapter 3.4 --- Visual Query Expansion --- p.22 / Chapter 3.5 --- Image Pool Expansion --- p.24 / Chapter 3.6 --- Textual Feature Combination --- p.26 / Chapter 4 --- Experimental Evaluation --- p.27 / Chapter 4.1 --- Dataset --- p.27 / Chapter 4.2 --- Experiment One: Evaluation with Ground Truth --- p.28 / Chapter 4.2.1 --- Precisions on Different Steps --- p.28 / Chapter 4.2.2 --- Accuracy of Keyword Expansion --- p.31 / Chapter 4.3 --- Experiment Two: User Study --- p.33 / Chapter 5 --- Conclusion --- p.39

Wong, Tik Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 57-62). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Thesis Contributions --- p.10 / Chapter 1.3 --- Thesis Organization --- p.11 / Chapter 2 --- Literature Review --- p.12 / Chapter 2.1 --- Related Work on Product Record Normalization --- p.12 / Chapter 2.2 --- Related Work on Information Extraction --- p.15 / Chapter 2.2.1 --- Information Extraction Methods for Unstructured Documents --- p.16 / Chapter 2.2.2 --- Wrappers for Information Extraction --- p.16 / Chapter 2.2.3 --- Supervised Methods for Information Extraction --- p.17 / Chapter 2.2.4 --- Semi-supervised Methods for Information Extraction --- p.20 / Chapter 2.2.5 --- Unsupervised Methods for Information Extraction --- p.21 / Chapter 2.2.6 --- Probabilistic Methods for Information Extraction --- p.23 / Chapter 3 --- Background and Problem Definition --- p.26 / Chapter 3.1 --- Background --- p.26 / Chapter 3.2 --- Problem Definition --- p.29 / Chapter 4 --- Our Approach --- p.31 / Chapter 4.1 --- Generative Model --- p.31 / Chapter 4.2 --- Our Inference Method --- p.34 / Chapter 5 --- Experiments --- p.41 / Chapter 5.1 --- Experimental Setup --- p.41 / Chapter 5.2 --- Experimental Results --- p.49 / Chapter 5.3 --- The Effect of Reference Product Prior --- p.52 / Chapter 5.4 --- The Effect of Layout Information --- p.53 / Chapter 6 --- Conclusions and Future Work --- p.55 / Bibliography --- p.57 / Chapter A --- Detailed Performance of Product Record Normalization --- p.63

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 6, 2010. Major professor: Dr. Michael Haungs. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "Aug 2009." Includes bibliographical references (p. 76-78).

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Frequent patterns; traversal patterns. Includes bibliographical references (p. 50-52).

Not available / text

Thesis (Ph.D.)--Victoria University (Melbourne, Vic.), 2008.

Thesis (Ph. D.)--Michigan State University. Dept. of Psychology, 2008. / Title from PDF t.p. (viewed on July 8, 2009) Includes bibliographical references (p. 142-147). Also issued in print.