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

More Archives, More Better

PDF of a powerpoint presentation from the International Internet Preservation Consortium (IIPC) 2013 General Assembly in Ljubljana, Slovenia, April 23, 2013. Also available from Slideshare.https://digitalcommons.odu.edu/computerscience_presentations/1018/thumbnail.jp

MementoMap: A Web Archive Profiling Framework for Efficient Memento Routing

With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as to support routing of requests in Memento aggregators. A memento is a past version of a web page and a Memento aggregator is a tool or service that aggregates mementos from many different web archives. To save resources, the Memento aggregator should only poll the archives that are likely to have a copy of the requested Uniform Resource Identifier (URI). Using the Crawler Index (CDX), we generate profiles of the archives that summarize their holdings and use them to inform routing of the Memento aggregator’s URI requests. Additionally, we use full text search (when available) or sample URI lookups to build an understanding of an archive’s holdings. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. For evaluation we used CDX files from Archive-It, UK Web Archive, Stanford Web Archive Portal, and Arquivo.pt. Moreover, we used web server access log files from the Internet Archive’s Wayback Machine, UK Web Archive, Arquivo.pt, LANL’s Memento Proxy, and ODU’s MemGator Server. In addition, we utilized historical dataset of URIs from DMOZ. In early experiments with various URI-based static profiling policies we successfully identified about 78% of the URIs that were not present in the archive with less than 1% relative cost as compared to the complete knowledge profile and 94% URIs with less than 10% relative cost without any false negatives. In another experiment we found that we can correctly route 80% of the requests while maintaining about 0.9 recall by discovering only 10% of the archive holdings and generating a profile that costs less than 1% of the complete knowledge profile. We created MementoMap, a framework that allows web archives and third parties to express holdings and/or voids of an archive of any size with varying levels of details to fulfil various application needs. Our archive profiling framework enables tools and services to predict and rank archives where mementos of a requested URI are likely to be present. In static profiling policies we predefined the maximum depth of host and path segments of URIs for each policy that are used as URI keys. This gave us a good baseline for evaluation, but was not suitable for merging profiles with different policies. Later, we introduced a more flexible means to represent URI keys that uses wildcard characters to indicate whether a URI key was truncated. Moreover, we developed an algorithm to rollup URI keys dynamically at arbitrary depths when sufficient archiving activity is detected under certain URI prefixes. In an experiment with dynamic profiling of archival holdings we found that a MementoMap of less than 1.5% relative cost can correctly identify the presence or absence of 60% of the lookup URIs in the corresponding archive without any false negatives (i.e., 100% recall). In addition, we separately evaluated archival voids based on the most frequently accessed resources in the access log and found that we could have avoided more than 8% of the false positives without introducing any false negatives. We defined a routing score that can be used for Memento routing. Using a cut-off threshold technique on our routing score we achieved over 96% accuracy if we accept about 89% recall and for a recall of 99% we managed to get about 68% accuracy, which translates to about 72% saving in wasted lookup requests in our Memento aggregator. Moreover, when using top-k archives based on our routing score for routing and choosing only the topmost archive, we missed only about 8% of the sample URIs that are present in at least one archive, but when we selected top-2 archives, we missed less than 2% of these URIs. We also evaluated a machine learning-based routing approach, which resulted in an overall better accuracy, but poorer recall due to low prevalence of the sample lookup URI dataset in different web archives. We contributed various algorithms, such as a space and time efficient approach to ingest large lists of URIs to generate MementoMaps and a Random Searcher Model to discover samples of holdings of web archives. We contributed numerous tools to support various aspects of web archiving and replay, such as MemGator (a Memento aggregator), Inter- Planetary Wayback (a novel archival replay system), Reconstructive (a client-side request rerouting ServiceWorker), and AccessLog Parser. Moreover, this work yielded a file format specification draft called Unified Key Value Store (UKVS) that we use for serialization and dissemination of MementoMaps. It is a flexible and extensible file format that allows easy interactions with Unix text processing tools. UKVS can be used in many applications beyond MementoMaps

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

A Framework for Verifying the Fixity of Archived Web Resources

The number of public and private web archives has increased, and we implicitly trust content delivered by these archives. Fixity is checked to ensure that an archived resource has remained unaltered (i.e., fixed) since the time it was captured. Currently, end users do not have the ability to easily verify the fixity of content preserved in web archives. For instance, if a web page is archived in 1999 and replayed in 2019, how do we know that it has not been tampered with during those 20 years? In order for the users of web archives to verify that archived web resources have not been altered, they should have access to fixity information associated with these resources. However, most web archives do not allow accessing fixity information and, more importantly, even if fixity information is available, it is provided by the same archive delivering the resource, not by an independent archive or service. In this research, we present a framework for establishing and checking the fixity on the playback of archived resources, or mementos. The framework defines an archive-aware hashing function that consists of several guidelines for generating repeatable fixity information on the playback of mementos. These guidelines are results of our 14-month study for identifying and quantifying changes in replayed mementos over time that affect generating repeatable fixity information. Changes on the playback of mementos may be caused by JavaScript, transient errors, inconsistency in the availability of mementos over time, and archive-specific resources. Changes are also caused by transformations in the content of archived resources applied by web archives to appropriately replay these resources in a user’s browser. The study also shows that only 11.55% of mementos always produce the same fixity information after each replay, while about 16.06% of mementos always produce different fixity information after each replay. The remaining 72.39% of mementos produce multiple unique fixity information. We also find that mementos may disappear when web archives move to different domains or archives. In addition to defining multiple guidelines for generating fixity information, the framework introduces two approaches, Atomic and Block, that can be used to disseminate fixity information to web archives. The main difference between the two approaches is that, in the Atomic approach, the fixity information of each archived web page is stored in a separate file before being disseminated to several on-demand web archives, while in the Block approach, we batch together fixity information of multiple archived pages to a single binary-searchable file before being disseminated to archives. The framework defines the structure of URLs used to publish fixity information on the web and retrieve archived fixity information from web archives. Our framework does not require changes in the current web archiving infrastructure, and it is built based on well-known web archiving standards, such as the Memento protocol. The proposed framework will allow users to generate fixity information on any archived page at any time, preserve the fixity information independently from the archive delivering the archived page, and verify the fixity of the archived page at any time in the future

Aggregating Private and Public Web Archives Using the Mementity Framework

Web archives preserve the live Web for posterity, but the content on the Web one cares about may not be preserved. The ability to access this content in the future requires the assurance that those sites will continue to exist on the Web until the content is requested and that the content will remain accessible. It is ultimately the responsibility of the individual to preserve this content, but attempting to replay personally preserved pages segregates archived pages by individuals and organizations of personal, private, and public Web content. This is misrepresentative of the Web as it was. While the Memento Framework may be used for inter-archive aggregation, no dynamics exist for the special consideration needed for the contents of these personal and private captures. In this work we introduce a framework for aggregating private and public Web archives. We introduce three mementities that serve the roles of the aforementioned aggregation, access control to personal Web archives, and negotiation of Web archives in dimensions beyond time, inclusive of the dimension of privacy. These three mementities serve as the foundation of the Mementity Framework. We investigate the difficulties and dynamics of preserving, replaying, aggregating, propagating, and collaborating with live Web captures of personal and private content. We offer a systematic solution to these outstanding issues through the application of the framework. We ensure the framework\u27s applicability beyond the use cases we describe as well as the extensibility of reusing the mementities for currently unforeseen access patterns. We evaluate the framework by justifying the mementity design decisions, formulaically abstracting the anticipated temporal and spatial costs, and providing reference implementations, usage, and examples for the framework

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

Detecting, Modeling, and Predicting User Temporal Intention

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, elections, war, disasters, and others as shown in this work. The user sharing the resource has an implicit temporal intent: either the state of the resource at the time of sharing, or the current state of the resource at the time of the reader \clicking . In this research, we propose a model to detect and predict the user\u27s temporal intention of the author upon sharing content in the social network and of the reader upon resolving this content. To build this model, we first examine the three aspects of the problem: the resource, time, and the user. For the resource we start by analyzing the content on the live web and its persistence. We noticed that a portion of the resources shared in social media disappear, and with further analysis we unraveled a relationship between this disappearance and time. We lose around 11% of the resources after one year of sharing and a steady 7% every following year. With this, we turn to the public archives and our analysis reveals that not all posted resources are archived and even they were an average 8% per year disappears from the archives and in some cases the archived content is heavily damaged. These observations prove that in regards to archives resources are not well-enough populated to consistently and reliably reconstruct the missing resource as it existed at the time of sharing. To analyze the concept of time we devised several experiments to estimate the creation date of the shared resources. We developed Carbon Date, a tool which successfully estimated the correct creation dates for 76% of the test sets. Since the resources\u27 creation we wanted to measure if and how they change with time. We conducted a longitudinal study on a data set of very recently-published tweet-resource pairs and recording observations hourly. We found that after just one hour, ~4% of the resources have changed by ≥30% while after a day the change rate slowed to be ~12% of the resources changed by ≥40%. In regards to the third and final component of the problem we conducted user behavioral analysis experiments and built a data set of 1,124 instances manually assigned by test subjects. Temporal intention proved to be a difficult concept for average users to understand. We developed our Temporal Intention Relevancy Model (TIRM) to transform the highly subjective temporal intention problem into the more easily understood idea of relevancy between a tweet and the resource it links to, and change of the resource through time. On our collected data set TIRM produced a significant 90.27% success rate. Furthermore, we extended TIRM and used it to build a time-based model to predict temporal intention change or steadiness at the time of posting with 77% accuracy. We built a service API around this model to provide predictions and a few prototypes. Future tools could implement TIRM to assist users in pushing copies of shared resources into public web archives to ensure the integrity of the historical record. Additional tools could be used to assist the mining of the existing social media corpus by derefrencing the intended version of the shared resource based on the intention strength and the time between the tweeting and mining