USING SOCIAL ANNOTATIONS TO IMPROVE WEB SEARCH

Web-based tagging systems, which include social bookmarking systems such as Delicious, have become increasingly popular. These systems allow participants to annotate or tag web resources. This research examined the use of social annotations to improve the quality of web searches. The research involved three components. First, social annotations were used to index resources. Two annotation-based indexing methods were proposed: annotation based indexing and full text with annotation indexing. Second, social annotations were used to improve search result ranking. Six annotation based ranking methods were proposed: Popularity Count, Propagate Popularity Count, Query Weighted Popularity Count, Query Weighted Propagate Popularity Count, Match Tag Count and Normalized Match Tag Count. Third, social annotations were used to both index and rank resources. The result from the first experiment suggested that both static feature and similarity feature should be considered when using social annotations to re-rank search result. The result of the second experiment showed that using only annotation as an index of resources may not be a good idea. Since social Annotations could be viewed as a high level concept of the content, combining them to the content of resource could add some more important concepts to the resources. Last but not least, the result from the third experiment confirmed that the combination of using social annotations to rank the search result and using social annotations as resource index augmentation provided a promising rank of search results. It showed that social annotations could benefit web search

User-Generated Tagging and Segmentation of Video Records of Practice: A Tool for Meaning-Marking.

The field of teacher education is consciously shifting its focus to be more “practice-oriented” and increasingly using video as a way of examining teaching practice. However, questions remain about how educators make sense of video and what types of tools and supports are needed. This exploratory study examines the potential of user-generated segmenting and tagging of videos of teaching practice as a tool for marking what educators find salient about teaching and the language they use to describe those phenomena. Data was collected in a teacher education program where video was used extensively for the purposes of learning about and improving teaching practice. There were two participant groups: pre-service teachers (n=6) and teacher educators/educational researchers (n=8). Each participant watched the same 8-minute video of practice and applied segments and tags to the video. The data included segments and tags created by each participant, interviews, and questionnaires; themes in the data were uncovered using content analysis. Interview data was used to interpret participants’ meaning in order to accurately categorize the tags. Using tag gardening strategies, hierarchal and networked tagging language was visualized. Findings indicate that user-generated segment and tag data of video records of practice can provide insight into what participants pay attention to and the language they use to describe that meaning making. This study uncovered three tensions that influenced participants’ segmenting and tagging behavior: findability versus nuance, concerns with being critical, and the need for a social context and community of practice. Educators’ specific and unique needs, purposes, and culture directly affected what participants marked as salient and what tagging language they used, resulting in some misleading segment and tag data. This work provides insights into the design of segmenting and tagging video tools and online communities of practice that support educators’ use of video. This research is particularly relevant to teacher education professionals and designers of tools that support educators’ use of video records of practice, laying the groundwork for further research on using and designing video annotation tools that support the work of teaching and aggregate data about how educators are making sense of videos of teaching.PHDEducational StudiesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116765/1/jrsteine_1.pd

The TicTag application: towards tag-based meta-search-for browsing the web

Computer Systems, Imagery and Medi

Detecting, Modeling, and Predicting User Temporal Intention

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, elections, war, disasters, and others as shown in this work. The user sharing the resource has an implicit temporal intent: either the state of the resource at the time of sharing, or the current state of the resource at the time of the reader \clicking . In this research, we propose a model to detect and predict the user\u27s temporal intention of the author upon sharing content in the social network and of the reader upon resolving this content. To build this model, we first examine the three aspects of the problem: the resource, time, and the user. For the resource we start by analyzing the content on the live web and its persistence. We noticed that a portion of the resources shared in social media disappear, and with further analysis we unraveled a relationship between this disappearance and time. We lose around 11% of the resources after one year of sharing and a steady 7% every following year. With this, we turn to the public archives and our analysis reveals that not all posted resources are archived and even they were an average 8% per year disappears from the archives and in some cases the archived content is heavily damaged. These observations prove that in regards to archives resources are not well-enough populated to consistently and reliably reconstruct the missing resource as it existed at the time of sharing. To analyze the concept of time we devised several experiments to estimate the creation date of the shared resources. We developed Carbon Date, a tool which successfully estimated the correct creation dates for 76% of the test sets. Since the resources\u27 creation we wanted to measure if and how they change with time. We conducted a longitudinal study on a data set of very recently-published tweet-resource pairs and recording observations hourly. We found that after just one hour, ~4% of the resources have changed by ≥30% while after a day the change rate slowed to be ~12% of the resources changed by ≥40%. In regards to the third and final component of the problem we conducted user behavioral analysis experiments and built a data set of 1,124 instances manually assigned by test subjects. Temporal intention proved to be a difficult concept for average users to understand. We developed our Temporal Intention Relevancy Model (TIRM) to transform the highly subjective temporal intention problem into the more easily understood idea of relevancy between a tweet and the resource it links to, and change of the resource through time. On our collected data set TIRM produced a significant 90.27% success rate. Furthermore, we extended TIRM and used it to build a time-based model to predict temporal intention change or steadiness at the time of posting with 77% accuracy. We built a service API around this model to provide predictions and a few prototypes. Future tools could implement TIRM to assist users in pushing copies of shared resources into public web archives to ensure the integrity of the historical record. Additional tools could be used to assist the mining of the existing social media corpus by derefrencing the intended version of the shared resource based on the intention strength and the time between the tweeting and mining

Email search visualization : An efficient way for searching email

Originally email was designed for messaging channel between individuals. However, it is now being used as personal file archiving. The growth of email quantity over time makes searching email hard and time-consuming. It even becomes extremely frustrating for people who have huge amount of emails. Firstly, when searching an email, people often look for a piece of information which is temporarily forgotten. If we can remind of them some related information, searching activity will become faster and easier. Secondly, since memory of information is temporarily lost, it is hard for people to define keyword to type in search box. If people misspell the word, search result will return no hits at all. In order to solve those issues, we provide an interface that support people seeking email more interactively. The interface is the combination of studies from email visualization and keyword extraction technique. Email visualization is the process of visually display email attributes such as sender, recipient, date, and content on the interface. Keyword extraction is a technique of extracting words directly from email body. Extracted keyword can describe related topical word which is meaningful and can be easily understood by users. Main objective of the interface is helping people to reinstate their memory of email information while visually interacting with different email attributes

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook

Utilizing distributed web resources for enhanced knowledge representation

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XBRL and the qualitative characteristics of useful financial statement information

Purpose of the Thesis The purpose of the thesis is to explore, identify, describe and evaluate technological and accounting issues and problems and their potential solutions that are related to the eXtensible Business Reporting Language (XBRL), together with providing some further research ideas. Research Methods and Data The thesis is conducted as a literature review of scientific journal articles and working papers. XBRL has emerged as a solution to many so-called “wicked” problems related to financial reporting in the Internet, a field where little theoretical understanding can a priori be taken for granted, and where pragmatic problem-solving procedures are needed to develop a solution that can be adopted for general use. The review follows the phases of a constructive Design Research process. Technological and accounting issues are discussed and evaluated at each phase, with the qualitative characteristics of useful financial statement information, relevance and faithful representation as the two fundamental qualitative characteristics and comparability and understandability as the most pertinent of the enhancing qualitative characteristics, used as the main accounting evaluation criteria. Results The results indicate that there still remain many types of significant technical deficiencies in the first officially filed XBRL financial statements. Moreover, XBRL seems to bring in new types of deficiencies, which jeopardize the faithful representation objective of financial statements. Consequently, new types of assurance assertions and procedures are being developed. The flexibility of both accounting standards and XBRL taxonomies seem to lead to severe interoperability and accounting comparability problems, which might be mitigated by for instance adopting strictly template-based accounting standards. Tentative results indicate that XBRL does enhance the usefulness of financial statements by making them more understandable to users, thereby helping them make better investment decisions. The mandatory adoption of XBRL seems to have affected market information conditions in many countries somewhat, but it has not been established yet that XBRL would be affecting the content or relevance of the financial statement information itself. XBRL is viewed by many constituencies as an enabling technology in a longer-term shift from a paper-based to electronic financial reporting paradigm. At present, however, XBRL can be viewed as a regulator-driven infrastructure project, and affordable end-user software will probably be needed for its adoption and acceptance among the investing public. Europe and Finland are lagging behind in introducing XBRL, which may actually help in the end by enabling learning from the mistakes of others

Generation of Classificatory Metadata for Web Resources using Social Tags

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata generation process, especially for semantic metadata. This research aims to find a method to categorize web resources using social tags as metadata. In this research, social tagging systems are a mechanism to allow non-professional catalogers to participate in metadata generation. Because social tags are not from a controlled vocabulary, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research examines ways to deal with those issues to obtain a set of tags representing the resource from the tags provided by users.Two measurements that measure the importance of a tag are introduced. Annotation Dominance (AD) is a measurement of how much a tag term is agreed to by users. Another is Cross Resources Annotation Discrimination (CRAD), a measurement to discriminate tags in the collection. It is designed to remove tags that are used broadly or narrowly in the collection. Further, the study suggests a process to identify and to manage compound tags. The research aims to select important annotations (meta-terms) and remove meaningless ones (noise) from the tag set. This study, therefore, suggests two main measurements for getting a subset of tags with classification potential. To evaluate the proposed approach to find classificatory metadata candidates, we rely on users' relevance judgments comparing suggested tag terms and expert metadata terms. Human judges rate how relevant each term is on an n-point scale based on the relevance of each of the terms for the given resource