Although the Internet Archive's Wayback Machine is the largest and most well-known web archive, there have been a number of public web archives that have emerged in the last several years. With varying resources, audiences and collection development policies, these archives have varying levels of overlap with each other. While individual archives can be measured in terms of number of URIs, number of copies per URI, and intersection with other archives, to date there has been no answer to the question "How much of the Web is archived?" We study the question by approximating the Web using sample URIs from DMOZ, Delicious, Bitly, and search engine indexes; and, counting the number of copies of the sample URIs exist in various public web archives. Each sample set provides its own bias. The results from our sample sets indicate that range from 35%-90% of the Web has at least one archived copy, 17%-49% has between 2-5 copies, 1%-8% has 6-10 copies, and 8%-63% has more than 10 copies in public web archives. The number of URI copies varies as a function of time, but no more than 31.3% of URIs are archived more than once per month.

Abstract. 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.

Web archives do not capture every resource on every page that they attempt to archive. This results in archived pages missing a portion of their embedded resources. These embedded resources have varying historic, utility, and importance values. The proportion of missing embedded resources does not provide an accurate measure of their impact on the Web page; some embedded resources are more important to the utility of a page than others. We propose a method to measure the relative value of embedded resources and assign a damage rating to archived pages as a way to evaluate archival success. In this paper, we show that Web users' perceptions of damage are not accurately estimated by the proportion of missing embedded resources. The proportion of missing embedded resources is a less accurate estimate of resource damage than a random selection. We propose a damage rating algorithm that provides closer alignment to Web user perception, providing an overall improved agreement with users on memento damage by 17% and an improvement by 51% if the mementos are not similarly damaged. We use our algorithm to measure damage in the Internet Archive, showing that it is getting better at mitigating damage over time (going from 0.16 in 1998 to 0.13 in 2013). However, we show that a greater number of important embedded resources (2.05 per memento on average) are missing over time.

Web archives do not always capture every resource on every page that they attempt to archive. This results in archived pages missing a portion of their embedded resources. These embedded resources have varying historic, utility, and importance values. The proportion of missing embedded resources does not provide an accurate measure of their impact on the Web page; some embedded resources are more important to the utility of a page than others. We propose a method to measure the relative value of embedded resources and assign a damage rating to archived pages as a way to evaluate archival success. In this paper, we show that Web users' perceptions of damage are not accurately estimated by the proportion of missing embedded resources. In fact, the proportion of missing embedded resources is a less accurate estimate of resource damage than a random selection. We propose a damage rating algorithm that provides closer alignment to Web user perception, providing an overall improved agreement with users on memento damage by 17 % and an improvement by 51 % if the mementos have a damage rating delta >0.30. We use our algorithm to measure damage in the Internet Archive, showing that it is getting better at mitigating damage over time (going from a damage ). However, we show that a greater number of important embedded resources (2.05 per memento on average) are missing over time. Alternatively, the damage in WebCite is increasing over time (going from 0.375 in 2007 to 0.475 in 2014), while the missing embedded resources remain constant (13 % of the resources are missing on average). Finally, we investigate the impact of JavaScript on the damage of the archives, showing that a crawler that can archive JavaScript-dependent representations will reduce memento damage by 13.5 %.

When users post links to web pages in Twitter there is a time delta between when the post was shared (ttweet) and when it was read (t click ). Ideally, when this time delta is small there is often no change in the page's state. However upon reading shared content in the past and due to the dynamic nature of the web, the page's state could change and the intention of the author need to be inferred. In this work, we enhance a prior temporal intention model and tackle its shortcomings by incorporating extended linguistic feature analysis, replacing the prior textual similarity measure with semantic similarity one based on latent topic detection trained on Wikipedia English corpus, and finally by enriching and balancing the training dataset. We uncovered three different intention behaviors in respect to time: Stable Intention, Changing Intention from current to past, and Undefined intention. Using these classes and only the information available at posting time from the tweet and the current state of the resource, we correctly predict the temporal intention classification and strength with 77% accuracy.