Social media content has grown exponentially in the recent years and the role of social media has evolved from just narrating life events to actually shaping them. In this paper we explore how many resources shared in social media are still available on the live web or in public web archives. By analyzing six different event-centric datasets of resources shared in social media in the period from June 2009 to March 2012, we found about 11 lost and 20 archived after just a year and an average of 27 lost and 41 archived after two and a half years. Furthermore, we found a nearly linear relationship between time of sharing of the resource and the percentage lost, with a slightly less linear relationship between time of sharing and archiving coverage of the resource. From this model we conclude that after the first year of publishing, nearly 11 of shared resources will be lost and after that we will continue to lose 0.02 per day.
Further more, many concerns were raised about the persistence of shared resources and web content in general. Nelson and Allen studied the persistence of objects in a digital library and found that, with just over a year, 3 web resources referenced from papers in scholarly repositories using Memento and found that 28 of the web is archived and found it ranges from 16 on the starting seed URIs. examined the factors affecting reconstructing websites (using caches and archives) and found that PageRank, Age, and the number of hops from the top-level of the site were most influential @cite_2 .
@cite_2
2951582856 1510484544 2137187346 2748382489
In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45 (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28 of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems. The Web is ephemeral. Many resources have representations that change over time, and many of those representations are lost forever. A lucky few manage to reappear as archived resources that carry their own URIs. For example, some content management systems maintain version pages that reect a frozen prior state of their changing resources. Archives recurrently crawl the web to obtain the actual representation of resources, and subsequently make those available via special-purpose archived resources. In both cases, the archival copies have URIs that are protocolwise disconnected from the URI of the resource of which they represent a prior state. Indeed, the lack of temporal capabilities in the most common Web protocol, HTTP, prevents getting to an archived resource on the basis of the URI of its original. This turns accessing archived resources into a signicant discovery challenge for both human and software agents, which typically involves following a multitude of links from the original to the archival resource, or of searching archives for the original URI. This paper proposes the protocol-based Memento solution to address this problem, and describes a proof-of-concept experiment that includes major servers of archival content, including Wikipedia and the Internet Archive. The Memento solution is based on existing HTTP capabilities applied in a novel way to add the temporal dimension. The result is a framework in which archived resources can seamlessly be reached via the URI of their original: protocol-based time travel for the Web. The Memento Project's archive access additions to HTTP have enabled development of new web archive access user interfaces. After experiencing this web time travel, the in- evitable question that comes to mind is "How much of the Web is archived?" This question is studied by approximating the Web via sampling URIs from DMOZ, Delicious, Bitly, and search engine indexes and measuring number of archive copies available in various public web archives. The results indicate that 35 -90 of URIs have at least one archived copy, 17 -49 have two to five copies, 1 -8 have six to ten copies, and 8 -63 at least ten copies. The number of URI copies varies as a function of time, but only 14.6-31.3 of URIs are archived more than once per month. With its seemingly limitless scope, the World Wide Web promises enormous advantages, along with enormous problems, to researchers who seek to use it as a source of data. Websites change continually and a high level of flux makes it challenging to capture a snapshot of the web, or even a cross-section of a small subset of the web. A web archive, such as those at the Internet Archive, promises to store and deliver repeated cross-sections of the entire web, and it also offers the potential for longitudinal analysis. Whether this potential is realized depends on the extent to which the archive has truly captured the web. Therefore, a crucial question for Internet researchers is: ‘How good are the archival data?’ We ask if there are systematic biases in the Internet Archive, using a case study to address this question. Specifically, we are interested in whether biases exist in the British websites stored in the Internet Archive data. We find that the Internet Archive contains a surprisingly small subset, about 24 , of the webpages of the website that we use for our case study (the travel site, TripAdvisor). Furthermore, the subset of data we found in the Internet Archive appears to be biased and is not a random sample of the webpages on the site. The archived data we examine has a bias toward prominent webpages. This bias could create serious problems for research using archived websites.