Cristina Lopes

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Cristina Lopes is an author.

Cristina Lopes is an author.




Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Multi-platform image search using tag enrichment Document expansion
Image retrieval
Query formulation
Relevance feedback
SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval English 2012 The number of images available online is growing steadily and current web search engines have indexed more than 10 billion images. Approaches to image retrieval are still often text-based and operate on image annotations and captions. Image annotations (i.e. image tags) are typically short, user-generated, and of varying quality, which increases the mismatch problem between query terms and image tags. For example, a user might enter the query "wedding dress" while all images are annotated with "bridal gown" or "wedding gown". This demonstration presents an image search system using reduction and expansion of image annotations to overcome vocabulary mismatch problems by enriching the sparse set of image tags. Our image search application accepts a written query as input and produces a ranked list of result images and annotations (i.e. image tags) as output. The system integrates methods to reduce and expand the image tag set, thus decreasing the effect of sparse image tags. It builds on different image collections such as the Wikipedia image collection ( and the Microsoft ClipArt collection (, but can be applied to social collections such as Flickr as well. Our demonstration system runs on PCs, tablets, and smartphones, making use of advanced user interface capabilities on mobile devices. 0 0
Modeling user reputation in wikis Web 2.0
Wiki mining
Statistical Analysis and Data Mining English 2010 Collaborative systems available on the Web allow millions of users to share information through a growing collection of tools and platforms such as wikis, blogs, and shared forums. By their very nature, these systems contain resources and information with different quality levels. The open nature of these systems, however, makes it difficult for users to determine the quality of the available information and the reputation of its providers. Here, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user's reputation. Finally, we validate these models using ground-truth Wikipedia data associated with vandals and administrators. When used as a classifier, the best model produces an area under the receiver operating characteristic {(ROC)} curve {(AUC)} of 0.98. Furthermore, we assess the reputation predictions generated by the models on other users, and show that all three models can be used efficiently for predicting user behavior in Wikipedia. 0 2
Statistical measure of quality in Wikipedia Wikipedia
Collaborative authoring
Web 2.0
SOMA English 2010 0 2
Leveraging crowdsourcing heuristics to improve search in Wikipedia WikiSym English 2009 0 0
Review-Based Ranking of Wikipedia Articles Wikipedia
CASON English 2009 0 0
Review-based ranking of Wikipedia articles Ranking
CASoN 2009 - International Conference on Computational Aspects of Social Networks English 2009 Wikipedia, the largest encyclopedia on the Web, is often seen as the most successful example of crowdsourcing. The encyclopedic knowledge it accumulated over the years is so large that one often uses search engines, to find information in it. In contrast to regular Web pages, Wikipedia is fairly structured, and articles are usually accompanied with history pages, categories and talk pages. The meta-data available in these pages can be analyzed to gain a better understanding of the content and quality of the articles. We discuss how the rich meta-data available in wiki pages can be used to provide better search results in Wikipedia. Built on the studies on "Wisdom of Crowd" and the effectiveness of the knowledge collected by a large number of people, we investigate the effect of incorporating the extent of review of an article in the quality of rankings of the search results. The extent of review is measured by the number of distinct editors contributed to the articles and is extracted by processingWikipedia's history pages.We compare different ranking algorithms that explore combinations of text- relevancy, PageRank, and extent of review. The results show that the review-based ranking algorithm which combines the extent of review and text-relevancy outperforms the rest; it is more accurate and less computationally expensive compared to PageRank-based rankings. 0 0
User contribution and trust in Wikipedia English 2009 Wikipedia, one of the top ten most visited websites, is commonly viewed as the largest online reference for encyclopedic knowledge. Because of its open editing model -allowing anyone to enter and edit content- Wikipedia's overall quality has often been questioned as a source of reliable information. Lack of study of the open editing model of Wikipedia and its effectiveness has resulted in a new generation of wikis that restrict contributions to registered users only, using their real names. In this paper, we present an empirical study of user contributions to Wikipedia. We statistically analyze contributions by both anonymous and registered users. The results show that submissions of anonymous and registered users in Wikipedia suggest a power law behavior. About 80% of the revisions are submitted by less than 7% of the users, most of whom are registered users. To further refine the analyzes, we use the Wiki Trust Model (WTM), a user reputation model developed in our previous work to assign a reputation value to each user. As expected, the results show that registered users contribute higher quality content and therefore are assigned higher reputation values. However, a significant number of anonymous users also contribute high-quality content.We provide further evidence that regardless of a user s' attribution, registered or anonymous, high reputation users are the dominant contributors that actively edit Wikipedia articles in order to remove vandalism or poor quality content. 0 2