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with more than 845 million facebook users at the end of 2011 @xcite and over 140 million tweets sent daily in 2011 @xcite users can take photos , videos , post their opinions , and report incidents as they happen .
many of the posts and tweets are about quotidian events and their preservation is debatable .
however , some of the posts and events are about culturally important events whose preservation is less controversial . in this paper
we shed light on the importance of archiving social media content about these events and estimate how much of this content is archived , still available , or lost with no possibility of recovery .
+ to emphasize the culturally important commentary and sharing , we collected data about six events in the time period of june 2009 to march 2012 : the h1n1 virus outbreak , michael jackson s death , the iranian elections and protests , barack obama s nobel peace prize , the egyptian revolution , and the syrian uprising .
to our knowledge , no prior study has analyzed the amount of shared resources in social media lost through time .
there have been many studies analyzing the behavior of users within a social network , how they interact , and what content they share @xcite . as for twitter ,
kwak et al .
@xcite studied its nature and its topological characteristics and found a deviation from known characteristics of human social networks that were analyzed by newman and park @xcite .
lee analyzed the reasons behind sharing news in social media and found that informativeness was the strongest motivation in predicting news sharing intention , followed by socializing and status seeking @xcite .
also shared content in social media like twitter move and diffuse relatively fast as stated by yang et al .
+ 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% of the sample they collected have appeared to no longer be available @xcite .
sanderson et al . analyzed the persistence and availability of web resources referenced from papers in scholarly repositories using memento and found that 28% of these resources have been lost @xcite .
memento @xcite is a collection of http extensions that enables uniform , inter - archive access .
ainsworth et al .
@xcite examined how much of the web is archived and found it ranges from 16% to 79% , depending on the starting seed uris .
mccown et al .
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 @xcite .
we compiled a list of uris that were shared in social media and correspond to specific culturally important events . in this section
we describe the data acquisition and sampling process we performed to extract six different datasets which will be tested and analyzed in the following sections .
the stanford large network dataset is a collection of about 50 large network datasets having millions of nodes , edges and tuples .
it was collected as a part of the stanford network analysis platform ( snap ) project @xcite .
it includes social networks , web graphs , road networks , internet networks , citation networks , collaboration networks , and communication networks . for the purpose of our investigation , we selected their twitter posts dataset .
this dataset was collected from june 1st , 2009 to december 31st , 2009 and contains nearly 476 million tweets posted by nearly 17 million users .
the dataset is estimated to cover 20%-30% of all posts published on twitter during that time frame@xcite . to select
which events will be covered in this study , we examined cnn s 2009 events timeline .
we wanted to select a small number of events that were diverse , with limited overlap , and relatively important to a large number of people . given that , we selected four events : the h1n1 virus outbreak , the iranian protests and elections , michael jackson s death , and barrack obama s nobel peace prize award .      a tweet is typically composed of text , hashtags , embedded resources or uris and usertags all spanning a maximum of 140 characters .
here is an example of a tweet record in the snap dataset :    * t * 2009 - 07 - 31 23:57:18 + * u * http://twitter.com/nickgotch + * w * rt @rockingjude : december 21 , 2009 depopulation by food will begin + http://is.gd/1wmzb whoa .. better watch rt plz # pwa # tcot + -5pt    the line starting with the letter * t * indicates the date and time of the tweet creation . while the line starting with * u * shows a link to the user who authored this particular tweet .
finally , the line starting with * w * shows the entire tweet including all the user - references `` @rockingjude '' , the embedded uris `` http://is.gd/1wmzb '' , and hashtags `` # pwa # tcot '' .
we wanted to select tweets that we can say with high confidence are about a selected event . in this case
, precision is more important than recall as collecting every single tweet published about a certain event is less important than making sure that the selected tweets are definitely about that event .
several studies focused on estimating the aboutness of a certain web page or a resource in general @xcite . fortunately in twitter
, hashtags incorporated within a tweet can help us estimate their `` _ _ aboutness _ _ '' .
users normally add certain hashtags to their tweets to ease the search and discoverability in following a certain topic .
these hashtags will be utilized in the event - centric filtration process . + for each event , we selected initial tags that describe it ( table [ tab : tags ] ) .
those initial tags were derived empirically after examining some event - related tweets .
next we extracted all the hashtags that co - occurred with our initial set of hashtags .
for example , in class h1n1 we extracted all the other hashtags that appeared along with _
# h1n1 _ within the same tweet and kept count of their frequency .
those extracted hashtags were sorted in descending order of the frequency of their appearance in tweets .
we removed all the general scope tags like _ # cnn _ , _ # health _ , _ # death _ , _ # war _ and others . in regards to aboutness , removing general tags will indeed decrease recall but will increase precision .
finally we picked the top 8 - 10 hashtags to represent this event - class and be utilized in the filtration process .
table [ tab : tags ] shows the final set of tags selected for each class .    -21pt
.twitter hashtags generated for filtering and their frequency of occurring [ cols="<,<,<",options="header " , ]      +    -50pt    -15pt   -15pt    -20pt [ fig : thebigfigure ]    figure [ fig : thebigfigure ] shows the missing and archived values from table [ tab : split ] as a function of time since shared .
equation [ missing ] shows the modeled estimate for the percentage of shared resources lost , where _ age _ is in days .
while there is a less linear relationship between time and being archived , equation [ archived ] shows the modeled estimate for the percentage of shared resources archived in a public archive .
@xmath0 @xmath1 given these observations and our curve fitting we estimate that after a year from publishing about 11% of content shared in social media will be gone .
after this point , we are losing roughly 0.02% of this content per day .
we can conclude that there is a nearly linear relationship between time of sharing in the social media and the percentage lost . although not as linear , there is a similar relationship between the time of sharing and the expected percentage of coverage in the archives . to reach this conclusion , we extracted collections of tweets and other social media content that was posted and shared in relation to six different events that occurred in the time period from june 2009 to march 2012 .
next we extracted the embedded resources within this social media content and tested their existence on the live web and in the archives . after analyzing the percentages lost and archived in relation to time and plotting them we used a linear regression model to fit those points .
finally we presented two linear models that can estimate the existence of a resource , that was posted or shared at one point of time in the social media , on the live web and in the archives as a function of age in the social media .
+ in the next stage of our research we need to expand the datasets and import other similar datasets especially in the uncovered temporal areas ( e.g. , the year of 2010 and before 2009 ) .
examining more datasets across extended points in time could enable us to better model these two functions of time .
also several other factors beside time would be analyzed to understand their effect on persistence on the live web and archiving coverage like : publishing venue , rate of sharing , popularity of authors and the nature of the related event . -25pt
this work was supported in part by the library of congress and nsf iis-1009392 .
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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 .    
 archiving , social media , digital preservation