BibSonomy publications for /tag/contentThu Jan 26 14:37:39 CET 2012How Bad Do You Spell?: The Lexical Quality of Social Media2011content of quality spelling, Mon Dec 19 16:57:24 CET 2011Berlin / HeidelbergResearch and Advanced Technology for Digital Libraries38-49Lecture Notes in Computer ScienceTrustworthiness Analysis of Web Search Results46752007User-generated analysis bisibs content results search trustworthiness web Increased usage of Web search engines in our daily lives means that the trustworthiness of searched results has become crucial. User studies on the usage of search engines and analysis of the factors used to determine trust that users have in search results are described in this paper. Based on the analysis, we developed a system to help users determine the trustworthiness of Web search results by computing and showing each returned page’s topic majority, topic coverage, locality of supporting pages (i.e., pages linked to each search result) and other information. The measures proposed in the paper can be applied to the search of Web-based libraries or can be useful in the usage of digital library search systems.Sat Dec 17 12:21:47 CET 2011New York, NY, USAJournal of the American Society for Information Science and TechnologyJanuary1117--132Ranking of Wikipedia articles in search engines revisited: Fair ranking for reasonable quality?622011Fair Ranking Wikipedia articles bisibs content engines generated quality ranking reasonable search ugc user This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation. © 2011 Wiley Periodicals, Inc.Thu Dec 15 13:13:14 CET 2011Journal of the American Society for Information Science and Technology71243--1256Judging the quality and credibility of information in Internet discussion forums622011Bisisbs Internet Judging content credibility discussion forums generated information quality user Wed Dec 14 17:14:07 CET 2011Los Alamitos, CA, USAComputerAugust63--72Toward a PeopleWeb402007PeopleWeb Toward a bisibs content generated user Important properties of users and objects will move from being tied to individual Web sites to being globally available. The conjunction of a global object model with portable user context will lead to a richer content structure and introduce significant shifts in online communities and information discovery.Wed Dec 14 11:35:14 CET 2011New York, NY, USAProceedings of the 20th international conference companion on World wide web327--328WWW '11Social media: source of information or bunch of noise2011Social bunch content generated information media: noise of or source user Social media has witnessed an explosive growth in the past few years. Wikipedia has over 3.5 million pages with descriptions of entities. Flickr members have uploaded over 5 billion photos,You Tube has 35 hours of videos uploaded to the site each minute, and Twitter users generate 65 million tweets a day. While some forms of social media like Wikipedia clearly have valuable information embedded in them, the jury is still out on other forms like tweets, comments, and social network (e.g., Facebook) updates. Some of the key questions that the panel will debate include: Is there useful information in social media like tweets? How to extract structured records from unstructured user-generated content like reviews? How to sift through the vast amounts of social media and filter out the spam/offensive content? How to rank social media like blogs and comments based on relevance or importance? How can social media be leveraged to achieve tasks like entity disambiguation, question answering, improved search, etc.? What are the novel Web applications where social media can be leveraged?Wed Dec 14 11:33:59 CET 2011New York, NY, USAProceedings of the 2nd ACM workshop on Information credibility on the web19--26WICOW '08Automatic scoring of online discussion posts2008Automatic content discussion generated of online posts scoring user Online discussions forums, known as forums for short, are conversational social cyberspaces constituting rich repositories of content and an important source of collaborative knowledge. However, most of this knowledge is buried inside the forum infrastructure and its extraction is both complex and difficult. The ability to automatically rate postings in online discussion forums, based on the value of their contribution, enhances the ability of users to find knowledge within this content. Several key online discussion forums have utilized collaborative intelligence to rate the value of postings made by users. However, a large percentage of posts go unattended and hence lack appropriate rating. In this paper, we focus on automatic rating of postings in online discussion forums. A set of features derived from the posting content and the threaded discussion structure are generated for each posting. These features are grouped into five categories, namely (i) relevance, (ii) originality, (iii) forum-specific features, (iv) surface features, and (v) posting-component features. Using a non-linear SVM classifier, the value of each posting is categorized into one of three levels High, Medium, or Low. This rating represents a seed value for each posting that is leveraged in filtering forum content. Experimental results have shown promising performance on forum data.Thu Dec 08 14:29:02 CET 2011Journal of Computer-Mediated Communication119--38“Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews172011Consumer Online Reviews characteristics content highly perceived recommended The aim of the present study was to gain a better understanding of the content characteristics that make online consumer reviews a useful source of consumer information. To this end, we content analyzed reviews of experience and search products posted on Amazon.com (N = 400). The insights derived from this content analysis were linked with the proportion of ‘useful’ votes that reviews received from fellow consumers. The results show that content characteristics are paramount to understanding the perceived usefulness of reviews. Specifically, argumentation (density and diversity) served as a significant predictor of perceived usefulness, as did review valence although this latter effect was contingent on the type of product (search or experience) being evaluated in reviews. The presence of expertise claims appeared to be weakly related to the perceived usefulness of reviews. The broader theoretical, methodological and practical implications of these findings are discussed.Thu Dec 08 13:27:01 CET 2011New York, NY, USAProceedings of the 3rd workshop on Information credibility on the web27--34WICOW '09QuWi: quality control in Wikipedia2009content generated quality user We propose and evaluate QuWi (Quality in Wikipedia), a framework for quality control in Wikipedia. We build upon a previous proposal by Mizzaro [11], who proposed a method for substituting and/or complementing peer review in scholarly publishing. Since articles in Wikipedia are never finished, and their authors change continuously, we define a modified algorithm that takes into account the different domain, with particular attention to the fact that authors contribute identifiable pieces of information that can be further modified by other authors. The algorithm assigns quality scores to articles and contributors. The scores assigned to articles can be used, e.g., to let the reader understand how reliable are the articles he or she is looking at, or to help contributors in identifying low quality articles to be enhanced. The scores assigned to users measure the average quality of their contributions to Wikipedia and can be used, e.g., for conflict resolution policies based on the quality of involved users. Our proposed algorithm is experimentally evaluated by analyzing the obtained quality scores on articles for deletion and featured articles, also on six temporal Wikipedia snapshots. Preliminary results demonstrate that the proposed algorithm seems to appropriately identify high and low quality articles, and that high quality authors produce more long-lived contributions than low quality authors.Thu Dec 08 13:26:16 CET 2011New York, NY, USAProceedings of the 3rd workshop on Information credibility on the web3--10WICOW '09Automatically assessing resource quality for educational digital libraries2009assessing automatically content educational generated quality resource user With the rise of community-generated web content, the need for automatic assessment of resource quality has grown. We demonstrate how developing a concrete characterization of quality for web-based resources can make machine learning approaches to automating quality assessment in the realm of educational digital libraries tractable. Using data from several previous studies of quality, we gathered a set of key dimensions and indicators of quality that were commonly identified by educators. We then performed a mixed-method study of digital library quality experts, showing that our characterization of quality captured the subjective processes used by the experts when assessing resource quality. Using key indicators of quality selected from a statistical analysis of our expert study data, we developed a set of annotation guidelines and annotated a corpus of 1000 digital resources for the presence or absence of the key quality indicators. Agreement among annotators was high, and initial machine learning models trained from this corpus were able to identify some indicators of quality with as much as an 18% improvement over the baseline.Thu Dec 08 13:24:57 CET 2011New York, NY, USAProceedings of the 3rd workshop on Information credibility on the web1--2WICOW '09User generated content: how good is it?2009bisibs content generated quality user User Generated Content (UGC) is one of the main current trends in the Web. This trend has allowed all people that can access the Internet to publish content in different media, such as text (e.g. blogs), photos or video. This data can be crucial for many applications, in particular for semantic search. It is early to say which impact UGC will have and to what extent. However, the impact will be clearly related to the quality of this content. Hence, how good is the content that people generate in the so called Web 2.0? Clearly is not as good as editorial content in the Web site of a publisher. However, histories of success such as the case of the Wikipedia, show that it can be quite good. In addition, the quality gap is balanced by volume, as user generated content is much larger than, say, editorial content. In fact, Ramakrishnan and Tomkins estimate that UGC generates daily from 8 to 10GB while the professional Web only generates 2GB in the same time. How we can estimate the quality of UGC? One possibility is to directly evaluate the quality, but that is not easy as depends on the type of content and the availability of human judgments. One example of such approach is the study of Yahoo! Answers done by Agichtein et al. In this work they start from a judged question/answer collection where good questions usually have good answers. Then they predict good questions and good answers, obtaining an AUC (area under the curve of the precision-recall graph) of 0.76 and 0.88, respectively. A second possibility is obtaining indirect evidence of the quality. For example, use UGC for a given task and then evaluate the quality of the task results. One such example is the extraction of semantic relations done by Baeza-Yates and Tiberi. To evaluate the quality of the results they used the Open Directory Project (ODP), showing that the results had a precision of over 60%. For the cases that were not found in the ODP, a manually verified sample showed that the real precision was close to 100%. What happened was that the ODP was not specific enough to contain very specific relations, and every day the problem gets worse as we have more data. This example shows the quality of ODP as well as the semantic encoded in queries. Notice that we can define queries as implicit UGC, because each query can be considered an implicit tag to Web pages that are clicked for that query, and hence we have an implicit folksonomy. A final alternative is crossing different UGC sources and infer from there the quality of those sources. An example of this case, is the work by Van Zwol et al. where they use collective knowledge (wisdom of crowds) to extend image tags, and prove that almost 70% of the tags can be semantically classified by using Wordnet and Wikipedia. This exposes the quality of both Flickr tags and Wikipedia. Our main motivation, is that by being able to generate semantic resources automatically from the Web (and in particular the Web 2.0), even with noise, coupling that with open content resources, we can create a virtuous feedback circuit. In fact, explicit and implicit folksonomies can be used to do supervised machine learning without the need of manual intervention (or at least drastically reduce it) to improve semantic tagging. After that, we can feedback the results on itself, and repeat the process. Using the right conditions, every iteration should improve the output, obtaining a virtuous cycle. As a side effect, we can also improve Web search, our main goal.Thu Dec 08 13:19:18 CET 2011London, UK, UKJ. Netw. Comput. Appl.March84--97Interest based selection of user generated content for rich communication services332010based bisibs content generated interest selection user The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval. On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience. In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories. In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model. Thu Dec 08 13:18:14 CET 2011Hingham, MA, USADistrib. Parallel DatabasesJune239--260Quantifying the trustworthiness of social media content292011Quantifying bisibs content media of social the trustworthiness The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-generated content. A significant portion of this information is useful and has proven to be a great source of knowledge. However, since much of this information has been contributed by strangers with little or no apparent reputation to speak of, there is no easy way to detect whether the content is trustworthy. Search engines are the gateways to knowledge but search relevance cannot guarantee that the content in the search results is trustworthy. A casual observer might not be able to differentiate between trustworthy and untrustworthy content. This work is focused on the problem of quantifying the value of such shared content with respect to its trustworthiness. In particular, the focus is on shared health content as the negative impact of acting on untrustworthy content is high in this domain. Health content from two social media applications, Wikipedia and Daily Strength, is used for this study. Sociological notions of trust are used to motivate the search for a solution. A two-step unsupervised, feature-driven approach is proposed for this purpose: a feature identification step in which relevant information categories are specified and suitable features are identified, and a quantification step for which various unsupervised scoring models are proposed. Results indicate that this approach is effective and can be adapted to disparate social media applications with ease.Thu Dec 08 12:58:47 CET 2011ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND{INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS}oct10645-655Junior physician's use of Web 2.0 for information seeking and medical education: A qualitative study{Article}78{2009}2.0 Bisibs Clinical Information Junior Medical Physicians User-generated Web content eHealth education seeking Background: Web 2.0 internet tools and methods have attracted considerable attention as a means to improve health care delivery. Despite evidence demonstrating their use by medical professionals, there is no detailed research describing how Web 2.0 influences physicians' daily clinical practice. Hence this study examines Web 2.0 use by 35 junior physicians in clinical settings to further understand their impact on medical practice. Method: Diaries and interviews encompassing 177 days of internet use or 444 search incidents, analyzed via thematic analysis. Results: Results indicate that 53\% of internet visits employed user-generated or Web 2.0 content, with Google and Wikipedia used by 80\% and 70\% of physicians, respectively. Despite awareness of information credibility risks with Web 2.0 content, it has a role in information seeking for both clinical decisions and medical education. This is enabled by the ability to cross check information and the diverse needs for background and non-verified information. Conclusion: Web 2.0 use represents a profound departure from previous learning and decision processes which were normally controlled by senior medical staff or medical schools. There is widespread concern with the risk of poor quality information with Web 2.0 use, and the manner in which physicians are using it suggest effective use derives from the mitigating actions by the individual physician. Three alternative policy options are identified to manage this risk and improve efficiency in Web 2.0's use. (C) 2009 Elsevier Ireland Ltd. All rights reserved.Thu Dec 08 11:28:57 CET 2011New York, NY, USAWSDM '08: Proceedings of the international conference on Web search and web data mining183--194Finding high-quality content in social media2008content generated quality social user Tue Nov 29 18:05:13 CET 2011American Journal of Health BehaviorSupplement 3S217-S226Studying the News on Public Health: How Content Analysis Supports Media Advocacy272003advocacy analisi analysis content contenuto giornalismo informazione media ricerca salute Objective: To describe how content analysis of the news assists media advocates. Methods: A description of how findings from the Berkeley Media Studies Group's research on how 2 public health issues have been portrayed in the news has informed media advocacy. Results: For media advocates, the research suggests they make themselves available to reporters, prepare spokespeople representing key stakeholders, and make data available. For reporters, the research suggests they expand sources beyond the usual suspects, provide context in regular reporting, increase enterprise and investigative reporting, and ask better questions based on epidemiology and risk factors. Conclusion: Content analysis can help media advocates pinpoint areas for creating news to advance policy.Mon Nov 28 17:51:50 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy myown recommendations recommender tag Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. Fri Nov 25 12:41:14 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy myown recommendations recommender tag Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. Fri Nov 25 12:41:06 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy itegpub l3s myown recommendations recommender tag tagorapub Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. Fri Nov 25 10:31:36 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 comparison content folksonomy recommendations taggingsurvey Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. contentCommunity for tag(s) content