Tools for Managing the Past Web

PDF of a powerpoint presentation from an Old Dominion University - ECE Department Seminar, February 20, 2015. Also available on Slideshare.https://digitalcommons.odu.edu/computerscience_presentations/1039/thumbnail.jp

Right HTML, Wrong JSON: Challenges in Replaying Archived Webpages Built with Client-Side Rendering

Many web sites are transitioning how they construct their pages. The conventional model is where the content is embedded server-side in the HTML and returned to the client in an HTTP response. Increasingly, sites are moving to a model where the initial HTTP response contains only an HTML skeleton plus JavaScript that makes API calls to a variety of servers for the content (typically in JSON format), and then builds out the DOM client-side, more easily allowing for periodically refreshing the content in a page and allowing dynamic modification of the content. This client-side rendering, now predominant in social media platforms such as Twitter and Instagram, is also being adopted by news outlets, such as CNN.com. When conventional web archiving techniques, such as crawling with Heritrix, are applied to pages that render their content client-side, the JSON responses can become out of sync with the HTML page in which it is to be embedded, resulting in temporal violations on replay. Because the violative JSON is not directly observable in the page (i.e., in the same manner a violative embedded image is), the temporal violations can be difficult to detect. We describe how the top level CNN.com page has used client-side rendering since April 2015 and the impact this has had on web archives. Between April 24, 2015 and July 21, 2016, we found almost 15,000 mementos with a temporal violation of more than 2 days between the base CNN.com HTML and the JSON responses used to deliver the content under the main story. One way to mitigate this problem is to use browser-based crawling instead of conventional crawlers like Heritrix, but browser-based crawling is currently much slower than non-browser-based tools such as Heritrix.Comment: 20 pages, preprint version of paper accepted at the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL

Bringing Web Time Travel to MediaWiki: An Assessment of the Memento MediaWiki Extension

We have implemented the Memento MediaWiki Extension Version 2.0, which brings the Memento Protocol to MediaWiki, used by Wikipedia and the Wikimedia Foundation. Test results show that the extension has a negligible impact on performance. Two 302 status code datetime negotiation patterns, as defined by Memento, have been examined for the extension: Pattern 1.1, which requires 2 requests, versus Pattern 2.1, which requires 3 requests. Our test results and mathematical review find that, contrary to intuition, Pattern 2.1 performs better than Pattern 1.1 due to idiosyncrasies in MediaWiki. In addition to implementing Memento, Version 2.0 allows administrators to choose the optional 200-style datetime negotiation Pattern 1.2 instead of Pattern 2.1. It also permits administrators the ability to have the Memento MediaWiki Extension return full HTTP 400 and 500 status codes rather than using standard MediaWiki error pages. Finally, version 2.0 permits administrators to turn off recommended Memento headers if desired. Seeing as much of our work focuses on producing the correct revision of a wiki page in response to a user's datetime input, we also examine the problem of finding the correct revisions of the embedded resources, including images, stylesheets, and JavaScript; identifying the issues and discussing whether or not MediaWiki must be changed to support this functionality.Comment: 23 pages, 18 figures, 9 tables, 17 listing

Using Web Archives to Enrich the Live Web Experience Through Storytelling

Much of our cultural discourse occurs primarily on the Web. Thus, Web preservation is a fundamental precondition for multiple disciplines. Archiving Web pages into themed collections is a method for ensuring these resources are available for posterity. Services such as Archive-It exists to allow institutions to develop, curate, and preserve collections of Web resources. Understanding the contents and boundaries of these archived collections is a challenge for most people, resulting in the paradox of the larger the collection, the harder it is to understand. Meanwhile, as the sheer volume of data grows on the Web, storytelling is becoming a popular technique in social media for selecting Web resources to support a particular narrative or story . In this dissertation, we address the problem of understanding the archived collections through proposing the Dark and Stormy Archive (DSA) framework, in which we integrate storytelling social media and Web archives. In the DSA framework, we identify, evaluate, and select candidate Web pages from archived collections that summarize the holdings of these collections, arrange them in chronological order, and then visualize these pages using tools that users already are familiar with, such as Storify. To inform our work of generating stories from archived collections, we start by building a baseline for the structural characteristics of popular (i.e., receiving the most views) human-generated stories through investigating stories from Storify. Furthermore, we checked the entire population of Archive-It collections for better understanding the characteristics of the collections we intend to summarize. We then filter off-topic pages from the collections the using different methods to detect when an archived page in a collection has gone off-topic. We created a gold standard dataset from three Archive-It collections to evaluate the proposed methods at different thresholds. From the gold standard dataset, we identified five behaviors for the TimeMaps (a list of archived copies of a page) based on the page’s aboutness. Based on a dynamic slicing algorithm, we divide the collection and cluster the pages in each slice. We then select the best representative page from each cluster based on different quality metrics (e.g., the replay quality, and the quality of the generated snippet from the page). At the end, we put the selected pages in chronological order and visualize them using Storify. For evaluating the DSA framework, we obtained a ground truth dataset of hand-crafted stories from Archive-It collections generated by expert archivists. We used Amazon’s Mechanical Turk to evaluate the automatically generated stories against the stories that were created by domain experts. The results show that the automatically generated stories by the DSA are indistinguishable from those created by human subject domain experts, while at the same time both kinds of stories (automatic and human) are easily distinguished from randomly generated storie

Scripts in a Frame: A Framework for Archiving Deferred Representations

Web archives provide a view of the Web as seen by Web crawlers. Because of rapid advancements and adoption of client-side technologies like JavaScript and Ajax, coupled with the inability of crawlers to execute these technologies effectively, Web resources become harder to archive as they become more interactive. At Web scale, we cannot capture client-side representations using the current state-of-the art toolsets because of the migration from Web pages to Web applications. Web applications increasingly rely on JavaScript and other client-side programming languages to load embedded resources and change client-side state. We demonstrate that Web crawlers and other automatic archival tools are unable to archive the resulting JavaScript-dependent representations (what we term deferred representations), resulting in missing or incorrect content in the archives and the general inability to replay the archived resource as it existed at the time of capture. Building on prior studies on Web archiving, client-side monitoring of events and embedded resources, and studies of the Web, we establish an understanding of the trends contributing to the increasing unarchivability of deferred representations. We show that JavaScript leads to lower-quality mementos (archived Web resources) due to the archival difficulties it introduces. We measure the historical impact of JavaScript on mementos, demonstrating that the increased adoption of JavaScript and Ajax correlates with the increase in missing embedded resources. To measure memento and archive quality, we propose and evaluate a metric to assess memento quality closer to Web users’ perception. We propose a two-tiered crawling approach that enables crawlers to capture embedded resources dependent upon JavaScript. Measuring the performance benefits between crawl approaches, we propose a classification method that mitigates the performance impacts of the two-tiered crawling approach, and we measure the frontier size improvements observed with the two-tiered approach. Using the two-tiered crawling approach, we measure the number of client-side states associated with each URI-R and propose a mechanism for storing the mementos of deferred representations. In short, this dissertation details a body of work that explores the following: why JavaScript and deferred representations are difficult to archive (establishing the term deferred representation to describe JavaScript dependent representations); the extent to which JavaScript impacts archivability along with its impact on current archival tools; a metric for measuring the quality of mementos, which we use to describe the impact of JavaScript on archival quality; the performance trade-offs between traditional archival tools and technologies that better archive JavaScript; and a two-tiered crawling approach for discovering and archiving currently unarchivable descendants (representations generated by client-side user events) of deferred representations to mitigate the impact of JavaScript on our archives. In summary, what we archive is increasingly different from what we as interactive users experience. Using the approaches detailed in this dissertation, archives can create mementos closer to what users experience rather than archiving the crawlers’ experiences on the Web

Evaluating Sliding and Sticky Target Policies by Measuring Temporal Drift in Acyclic Walks Through a Web Archive

When a user views an archived page using the archive’s user interface (UI), the user selects a datetime to view from a list. The archived web page, if available, is then displayed. From this display, the web archive UI attempts to simulate the web browsing experience by smoothly transitioning between archived pages. During this process, the target datetime changes with each link followed; drifting away from the datetime originally selected. When browsing sparselyarchived pages, this nearly-silent drift can be many years in just a few clicks. We conducted 200,000 acyclic walks of archived pages, following up to 50 links per walk, comparing the results of two target datetime policies. The Sliding Target policy allows the target datetime to change as it does in archiveUIssuchastheInternetArchive’sWaybackMachine. The Sticky Target policy, represented by the Memento API, keeps the target datetime the same throughout the walk. We found that the Sliding Target policy drift increases with the number of walk steps, number of domains visited, and choice (number of links available). However, the Sticky Target policy controls temporal drift, holding it to less than 30 days on average regardless of walk length or number of domains visited. The StickyTarget policy shows some increase as choice increases, but this may be caused by other factors. We conclude that based on walk length, the Sticky Target policy generally produces at least 30 days less drift than the Sliding Target policy