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Research and Advanced Technology for Digital Libraries

2011

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Abstract. The World Wide Web has a very dynamic character with resources constantly disappearing and (re-)surfacing. A ubiquitous result is the "404 Page not Found" error as the request for missing web pages. We investigate tags obtained from Delicious for the purpose of rediscovering such missing web pages with the help of search engines. We determine the best performing tag based query length, quantify the relevance of the results and compare tags to retrieval methods based on a page's content. We find that tags are only useful in addition to content based methods. We further introduce the notion of "ghost tags", terms used as tags that do not occur in the current but did occur in a previous version of the web page. One third of these ghost tags are ranked high in Delicious and also occurred frequently in the document which indicates their importance to both the user and the content of the document.

Section: Performance Compared To Content Based Queriessupporting

confidence: 86%

Section: Content and Link Based Methods To Rediscover Web Pagesmentioning

confidence: 99%

Find, New, Copy, Web, Page - Tagging for the (Re-)Discovery of Web Pages

Klein

Research and Advanced Technology for Digital Libraries

2011

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“…In our "Just-in-time" preservation research we discovered new locations of web pages that are missing in the current web [19]. We investigated a variety of techniques, including using page titles [22], tags [21], and lexical signatures [23], all of which could be used as queries to search engines to find replacement copies of the missing web page.…”

Section: Related Workmentioning

confidence: 99%

Predicting Temporal Intention in Resource Sharing

SalahEldeen

Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries

2015

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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.

“…In our "Justin-time" preservation research we discovered new locations of web pages that are missing in the current web [19]. We investigated a variety of techniques, including using page titles [22], tags [21], and lexical signatures [23], all of which could be used as queries to search engines to find replacement copies of the missing web page.…”

Section: Related Workmentioning

confidence: 99%

Reading the correct history?

SalahEldeen

Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries

2013

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The web is trapped in the "perpetual now", and when users traverse from page to page, they are seeing the state of the web resource (i.e., the page) as it exists at the time of the click and not necessarily at the time when the link was made. Thus, a temporal discrepancy can arise between the resource at the time the page author created a link to it and the time when a reader follows the link. This is especially important in the context of social media: the ease of sharing links in a tweet or Facebook post allows many people to author web content, but the space constraints combined with poor awareness by authors often prevents sufficient context from being generated to determine the intent of the post. If the links are clicked as soon as they are shared, the temporal distance between sharing and clicking is so small that there is little to no difference in content. However, not all clicks occur immediately, and a delay of days or even hours can result in reading something other than what the author intended. We introduce the concept of a user's temporal intention upon publishing a link in social media. We investigate the features that could be extracted from the post, the linked resource, and the patterns of social dissemination to model this user intention. Finally, we analyze the historical integrity of the shared resources in social media across time. In other words, how much is the knowledge of the author's intent beneficial in maintaining the consistency of the story being told through social posts and in enriching the archived content coverage and depth of vulnerable resources?