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In previous work we reported that resources linked in tweets disappeared atthe rate of 11% in the first year followed by 7.3% each year afterwards. Wealso found that in the first year 6.7%, and 14.6% in each subsequent year, ofthe resources were archived in public web archives. In this paper we revisitthe same dataset of tweets and find that our prior model still holds and thecalculated error for estimating percentages missing was about 4%, but we foundthe rate of archiving produced a higher error of about 11.5%. We alsodiscovered that resources have disappeared from the archives themselves (7.89%)as well as reappeared on the live web after being declared missing (6.54%). Wehave also tested the availability of the tweets themselves and found that10.34% have disappeared from the live web. To mitigate the loss of resources onthe live web, we propose the use of a "tweet signature". Using the Topsy API,we extract the top five most frequent terms from the union of all tweets abouta resource, and use these five terms as a query to Google. We found that usingtweet signatures results in discovering replacement resources with 70+% textualsimilarity to the missing resource 41% of the time.

Resurrecting My Revolution: Using Social Link Neighborhood in Bringing Context to the Disappearing Web