Abstract

Abstract
This paper describes the participation of FEUP, from the University of Porto, in the TREC 2010 Blog Track. FEUP participated in the baseline blog distillation task with work focused on the use of link features available in the TREC Blogs08 collection. The approach presented in this paper uses the link information available in most individual posts to amplify each post's score. Blog scores, and subsequent ranks, are obtained by combining individual post scores. We boost post scores using the in-degree of each post and the h-index of each blog. This results in an improvement of P@10, over our baseline, for the in-degree and the h-index runs. When compared to the in- degree, the h-index run results in higher performance values for each of the applied evaluation metrics.

Abstract
Wikis are popular tools commonly used to support distributed collaborative work. Wikis can be seen as virtual scrap-books that anyone can edit without having any specific technical know-how. The Wikipedia is a flagship example of a real-word application of wikis. Due to the large scale of Wikipedia it's difficult to easily grasp much of the information that is stored in this wiki. We address one particular aspect of this issue by looking at the revision history of each article. Plotting the revision activity in a timeline we expose the complete article's history in a easily understandable format. We present WIKICHANGES, a web-based application designed to plot an article's revision timeline in real time. WIKICHANGES also includes a web browser extension that incorporates activity sparklines in the real Wikipedia. Finally, we introduce a revisions summarization task that addresses the need to understand what occurred during a given set of revisions. We present a first approach to this task using tag clouds to present the revisions made. © 2008 ACM.

Abstract
Documents on the World Wide Web are dynamic entities. Mainstream information retrieval systems and techniques are primarily focused on the latest version a document, generally ignoring its evolution over time. In this work, we study the term frequency dynamics in web documents over their lifespan. We use the Wikipedia as a document collection because it is a broad and public resource and, more important, because it provides access to the complete revision history of each document. We investigate the progression of similarity values over two projection variables, namely revision order and revision date. Based on this investigation we find that term frequency in encyclopedic documents - i.e. comprehensive and focused on a single topic - exhibits a rapid and steady progression towards the document's current version. The content in early versions quickly becomes very similar to the present version of the document.

Abstract
Time has been successfully used as a feature in web information retrieval tasks. In this context, estimating a document's inception date or last update date is a necessary task. Classic approaches have used HTTP header fields to estimate a document's last update time. The main problem with this approach is that it is applicable to a small part of web documents. In this work, we evaluate an alternative strategy based on a document's neighborhood. Using a random sample containing 10,000 URLs from the Yahoo! Directory, we study each document's links and media assets to determine its age. If we only consider isolated documents, we are able to date 52% of them. Including the document's neighborhood, we are able to estimate the date of more than 86% of the same sample. Also, we find that estimates differ significantly according to the type of neighbors used. The most reliable estimates are based on the document's media assets, while the worst estimates are based on incoming links. These results are experimentally evaluated with a real world application using different datasets. Copyright 2007 ACM.

Abstract
While trying to understand and characterize users' behavior online, the temporal dimension has received little attention by the research community. This exploratory study uses two collections of web search queries to investigate the use of temporal information needs. Using state-of-the-art information extraction techniques we identify temporal expressions in these queries. We find that temporal expressions are rarely used (1.5% of queries) and, when used, they are related to current and past events. Also, there are specific topics where the use of temporal expressions is more visible.