In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events ranging from elections to disease outbreaks. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. Two main strategies adopted by curators for discovering seeds include scraping Web (e.g., Google) Search Engine Result Pages (SERPs) and social media (e.g., Twitter) SERPs. In this work, we studied three social media platforms in order to provide some insight on the characteristics of seeds generated from different sources. First, we developed a simple vocabulary for describing social media posts across different platforms. Second, we introduced a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share posts about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create micro-collections is an indication of editorial activity and a demonstration of domain expertise. Third, we generated 23,112 seed collections with text and hashtag queries from 449,347 social media posts from Reddit, Twitter, and Scoop.it. We collected in total 120,444 URIs from the conventional scraped SERP posts and micro-collections. We characterized the resultant seed collections across multiple dimensions including the distribution of URIs, precision, ages, diversity of webpages, etc. We showed that seeds generated by scraping SERPs had a higher median probability (0.63) of producing relevant URIs than micro-collections (0.5). However, micro-collections were more likely to produce seeds with a higher precision than conventional SERP collections for Twitter collections generated with hashtags. Also, micro-collections were more likely to produce older webpages and more non-HTML documents.
In a similar work, @cite_19 adapted some portions of the in a method to extract event-centric documents from Web archives based on a specialized focused extraction algorithm. They defined two broad kinds of events based on time: and . The goal of the event-centric extraction process is, given an event input and a Web archive, generate an interlinked collection of documents relevant to the input event that meet the . The differences of our research with Gossen's previous work @cite_16 transfer to this work. However, we adapted Gossen's categorization of events as either or , and we renamed to (Table ). Similar to , @cite_28 presented an approach for extracting event-centric sub-collection from Web Archives. Their method extracts documents not only related to the input event, but also documents describing related events (e.g., premises and consequences). 's method utilized Wikipedia pages as inputs to generate event-centric collections. In this work, however, we used Wikipedia references to generate our gold standard dataset.
@cite_28 @cite_19 @cite_16
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Web archives, such as the Internet Archive, preserve an unprecedented abundance of materials regarding major events and transformations in our society. In this paper, we present an approach for building event-centric sub-collections from such large archives, which includes not only the core documents related to the event itself but, even more importantly, documents describing related aspects (e.g., premises and consequences). This is achieved by identifying relevant concepts and entities from a knowledge base, and then detecting their mentions in documents, which are interpreted as indicators for relevance. We extensively evaluate our system on two diachronic corpora, the New York Times Corpus and the US Congressional Record; additionally, we test its performance on the TREC KBA Stream Corpus and on the TREC-CAR dataset, two publicly available large-scale web collections. Web archives are typically very broad in scope and extremely large in scale. This makes data analysis appear daunting, especially for non-computer scientists. These collections constitute an increasingly important source for researchers in the social sciences, the historical sciences and journalists interested in studying past events. However, there are currently no access methods that help users to efficiently access information, in particular about specific events, beyond the retrieval of individual disconnected documents. Therefore we propose a novel method to extract event-centric document collections from large scale Web archives. This method relies on a specialized focused extraction algorithm. Our experiments on the German Web archive (covering a time period of 19 years) demonstrate that our method enables the extraction of event-centric collections for different event types. Web archives capture the history of the Web and are therefore an important source to study how societ al developments have been reflected on the Web. However, the large size of Web archives and their temporal nature pose many challenges to researchers interested in working with these collections. In this work, we describe the challenges of working with Web archives and propose the research methodology of extracting and studying sub-collections of the archive focused on specific topics and events. We discuss the opportunities and challenges of this approach and suggest a framework for creating sub-collections.