Bias in the retrieval of documents can directly influence the information access of a digital library. In the worst case, systematic favoritism for a certain type of document can render other parts of the collection invisible to users. This potential bias can be evaluated by measuring the retrievability for all documents in a collection. Previous evaluations have been performed on TREC collections using simulated query sets. The question remains, however, how representative this approach is of more realistic settings. To address this question, we investigate the effectiveness of the retrievability measure using a large digitized newspaper corpus, featuring two characteristics that distinguishes our experiments from previous studies: (1) compared to TREC collections, our collection contains noise originating from OCR processing, historical spelling and use of language; and (2) instead of simulated queries, the collection comes with real user query logs including click data. First, we assess the retrievability bias imposed on the newspaper collection by different IR models. We assess the retrievability measure and confirm its ability to capture the retrievability bias in our setup. Second, we show how simulated queries differ from real user queries regarding term frequency and prevalence of named entities, and how this affects the retrievability results.

Digitized document collections often suffer from OCR errors that may impact a document's readability and retrievability. We studied the effects of correcting OCR errors on the retrievability of documents in a historic newspaper corpus of a digital library. We computed retrievability scores for the uncorrected documents using queries from the library's search log, and found that the document OCR character error rate and retrievability score are strongly correlated. We computed retrievability scores for manually corrected versions of the same documents, and report on differences in their total sum, the overall retrievability bias, and the distribution of these changes over the documents, queries and query terms. For large collections, often only a fraction of the corpus is manually corrected. Using a mixed corpus, we assess how this mix affects the retrievability of the corrected and uncorrected documents. The correction of OCR errors increased the number of documents retrieved in all conditions. The increase contributed to a less biased retrieval, even when taking the potential lower ranking of uncorrected documents into account. CCS CONCEPTS• Information systems → Query log analysis; Evaluation of retrieval results;

Web archives attempt to preserve the fast changing web, yet they will always be incomplete. Due to restrictions in crawling depth, crawling frequency, and restrictive selection policies, large parts of the Web are unarchived and, therefore, lost to posterity. In this paper, we propose an approach to uncover unarchived web pages and websites and to reconstruct different types of descriptions for these pages and sites, based on links and anchor text in the set of crawled pages. We experiment with this approach on the Dutch Web Archive and evaluate the usefulness of page and host-level representations of unarchived content. Our main findings are the following: First, the crawled web contains evidence of a remarkable number of unarchived pages and websites, potentially dramatically increasing the coverage of a Web archive. Second, the link and anchor text have a highly skewed distribution: popular pages such as home pages have The Open University, Ra'anana, Israel more links pointing to them and more terms in the anchor text, but the richness tapers off quickly. Aggregating web page evidence to the host-level leads to significantly richer representations, but the distribution remains skewed. Third, the succinct representation is generally rich enough to uniquely identify pages on the unarchived web: in a known-item search setting we can retrieve unarchived web pages within the first ranks on average, with host-level representations leading to further improvement of the retrieval effectiveness for websites.

Web archives preserve the fast changing Web, yet are highly incomplete due to crawling restrictions, crawling depth and frequency, or restrictive selection policies-most of the Web is unarchived and therefore lost to posterity. In this paper, we propose an approach to recover significant parts of the unarchived Web, by reconstructing descriptions of these pages based on links and anchors in the set of crawled pages, and experiment with this approach on the Dutch Web archive.Our main findings are threefold. First, the crawled Web contains evidence of a remarkable number of unarchived pages and websites, potentially dramatically increasing the coverage of the Web archive. Second, the link and anchor descriptions have a highly skewed distribution: popular pages such as home pages have more terms, but the richness tapers off quickly. Third, the succinct representation is generally rich enough to uniquely identify pages on the unarchived Web: in a known-item search setting we can retrieve these pages within the first ranks on average.

A Web archive usually contains multiple versions of documents crawled from the Web at different points in time. One possible way for users to access a Web archive is through full-text search systems. However, previous studies have shown that these systems can induce a bias, known as the retrievability bias, on the accessibility of documents in community-collected collections (such as TREC collections). This bias can be measured by analyzing the distribution of the retrievability scores for each document in a collection, quantifying the likelihood of a document's retrieval. We investigate the suitability of retrievability scores in retrieval systems that consider every version of a document in a Web archive as an independent document. We show that the retrievability of documents can vary for different versions of the same document and that retrieval systems induce biases to different extents. We quantify this bias for a retrieval system which is adapted to handle multiple versions of the same document. The retrieval system indexes each version of a document independently, and we refine the search results using two techniques to aggregate similar versions. The first approach is to collapse similar versions of a document based on content similarity. The second approach is to collapse all versions of the same document based on their URLs. In both cases, we found that the degree of bias Universiteit Utrecht, Utrecht, The Netherlands is related to the aggregation level of versions of the same document. Finally, we study the effect of bias across time using the retrievability measure. Specifically, we investigate whether the number of documents crawled in a particular year correlates with the number of documents in the search results from that year. Assuming queries are not inherently temporal in nature, the analysis is based on the timestamps of documents in the search results returned using the retrieval model for all queries. The results show a relation between the number of documents per year and the number of documents retrieved by the retrieval system from that year. We further investigated the relation between the queries' timestamps and the documents' timestamps. First, we split the queries into different time frames using a 1-year granularity. Then, we issued the queries against the retrieval system. The results show that temporal queries indeed retrieve more documents from the assumed time frame. Thus, the documents from the same time frame were preferred by the retrieval system over documents from other time frames.