Losing My Revolution: How Many Resources Shared on Social Media Have Been Lost?

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.

Full text

Losing My Revolution
How Many Resources Shared on Social Media
Have Been Lost?
Hany M. SalahEldeen and Michael L. Nelson
Old Dominion University, Department of Computer Science
Norfolk VA, 23529, USA
{hany,mln}@cs.odu.edu
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.
Keywords: Web Archiving, Social Media, Digital Preservation
1 Introduction
With more than 845 million Facebook users at the end of 2011 [5] and over 140
million tweets sent daily in 2011 [16] 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 col-
lected 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.
arXiv:1209.3026v1 [cs.DL] 13 Sep 2012

2 Hany M. SalahEldeen and Michael L. Nelson
2 Related Work
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 [3, 19, 20, 23]. As for Twitter, Kwak et al. [6] 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 [10]. Lee
analyzed the reasons behind sharing news in social media and found that infor-
mativeness was the strongest motivation in predicting news sharing intention,
followed by socializing and status seeking [4]. Also shared content in social media
like Twitter move and diffuse relatively fast as stated by Yang et al. [22].
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 [9]. 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 [14]. Memento [17] is a collection of HTTP extensions that enables
uniform, inter-archive access. Ainsworth et al. [1] 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 [8].
3 Data Gathering
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 acqui-
sition and sampling process we performed to extract six different datasets which
will be tested and analyzed in the following sections.
3.1 Stanford SNAP Project Dataset
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 [15]. 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 [21]. To select which

Losing My Revolution 3
events will be covered in this study, we examined CNN’s 2009 events timeline1.
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.
Preparation: A tweet is typically composed of text, hashtags, embedded re-
sources or URIs and usertags all spanning a maximum of 140 characters. Here
is an example of a tweet record in the SNAP dataset:
T2009-07-31 23:57:18
Uhttp://Twitter.com/nickgotch
WRT @rockingjude: December 21, 2009 Depopulation by Food Will Begin
http://is.gd/1WMZb WHOA..BETTER WATCH RT plz #pwa #tcot
The line starting with the letter Tindicates the date and time of the tweet
creation. While the line starting with Ushows a link to the user who au-
thored this particular tweet. Finally, the line starting with Wshows the en-
tire tweet including all the user-references “@rockingjude”, the embedded URIs
“http://is.gd/1WMZb”, and hashtags “#pwa #tcot”.
Tag Expansion: We wanted to select tweets that we can say with high confi-
dence 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 [12, 18]. Fortunately in Twitter, hashtags incorporated
within a tweet can help us estimate their “aboutness ”. Users normally add cer-
tain 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 1). 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 1 shows the final set of tags selected for each class.
Tweet Filtration: In the previous step we extracted the tags that will help us
classify and filter tweets in the dataset according to each event. This filtration
1http://www.cnn.com/2009/US/12/16/year.timeline/index.html

4 Hany M. SalahEldeen and Michael L. Nelson
Event Initial Hashtags Top Co-occurring Hashtags
H1N1 ‘h1n1’ ‘swine’=61,829 ‘swineflu’=56,419 ‘flu’=8,436
Outbreak =61,351 ‘pandemic’=6,839 ‘influenza’=1,725 ‘grippe’=1,559 ‘tamiflu’=331
M. Jackson’s ‘michaeljackson’ ‘michael’=27,075 ‘mj’=18,584 ‘thisisit’8,770 ‘rip’=3,559 ‘jacko’=3,325
Death =22,934 ‘kingofpop’=2,888 ‘jackson’=2,559 ‘thriller’=1,357 ‘thankyoumichael’=1,050
Iranian ‘iranelection’ ‘iran’949,641 ‘gr88’=197,113‘tehran’=109,006 ‘freeiran’=13,378
Elections =911,808 ‘neda’=191,067 ‘mousavi’=16,587 ‘united4iran’=9,198 ‘iranrevolution’=7,295
Obama’s ‘obama’=48,161 & ‘nobel’=2,261 ‘obamanobel’=14 ‘nobelprize’ ‘nobelp eace’=113
Nobel Prize ‘peace‘=3,721 ‘barack’=1292 ‘nobelpeaceprize’=107
Table 1. Twitter hashtags generated for filtering and their frequency of occurring
process aims to extract a reasonable sized dataset of tweets for each event and to
minimize the inter-event overlap. Since the life and persistence of the tweet itself
is not the focus of this study but rather the associated resource that appears
in the tweet (image, video, shortened URI or other embedded resource), we will
extract only the tweets that contain an embedded resource. This step resulted in
181 million tweets with embedded resources (http://is.gd/1WMZb in the prior
example). These tweets were further filtered to keep only the tweets that have
at least one of the expanded tags obtained from Table 1. The number of tweets
after this phase reached 1.1 million tweets.
Filtering the tweets based on the occurrence of at least one of the hashtags
only is undesirable as it will cause two problems: First, it will introduce possible
event overlap due to general tweets talking about two or more topics. Second,
is that using only the single occurrence of these tags will yield a huge amount
of tweets and we need to reduce this size to reach a more manageable size. In-
tuitively speaking, strongly related hashtags will co-occur often. For example,
a tweet that has #h1n1 along with #swineflu and #pandemic is most likely
about the H1N1 outbreak rather than a tweet having just the tag #flu or just
#sick. Filtering with this co-occurrence will in turn solve both problems as by
increasing relevance to a particular event, general tweets that talk about several
events will be filtered out thus diminishing the overlap, and in turn it will reduce
the size of the dataset.
Next, we increase the precision of the tweets associated with each event from
the set of 1.1 million tweets. In the first iteration we selected the tag that had the
highest frequency of co-occurrence in the dataset with the initial tag and added
it to a set we will call the selection set. After that we check the co-occurrence
of all the remaining extracted tags with the tag in the selection set and record
the frequencies of co-occurrence. After sorting the frequencies of co-occurrence
with the tag from the selection set, we pick the highest one to keep add it to
the selection set. We repeat this step of counting co-occurrences but with all the
previously extracted hashtags in the selection set from previous iterations.
To elaborate, for H1N1 assume that the hastag ‘#h1n1’ had the highest
frequency of appearance in the dataset so we add it to the selection set. In the

Losing My Revolution 5
next iteration we record the how many times each tag in the list appeared along
with ‘#h1n1’ in a same tweet. If we selected ‘#swine’ as the one with the highest
frequency of occurrence with the initial tag ‘#h1n1’ we add it to the selection list
and in the next iteration we record the frequency of occurrence of the remaining
hashtags with both of the extracted tags ‘#h1n1’ and ‘#swine’. We repeat this
step, for each event, to the point where we have a manageable size dataset which
we are confident in its ‘aboutness’ in relation to the event.
Event Hashtags selected for filteration Tweets Extracted Operation Performed Final Tweets
MJ michael 27,075
michael & michaeljackson 22,934 Sample 10% 2,293
Iran iran 949,641
iran & iranelection 911,808
iran & iranelection & gr88 189,757
iran & iranelection & gr88 & neda 91,815
iran & iranelection & gr88 & neda & tehran 34,294 Sample 10% 3,429
H1N1 h1n1 61,351
h1n1 & swine 44,972
h1n1 & swine & swineflu 42,574
h1n1 & swine & swineflu & pandemic 5,517 Take All 5,517
Obama obama 48,161
obama & nobel 1,118 Take All 1,118
Table 2. Tweet Filtration iterations and final tweet collections
Two problems appeared from this approach with the Iran and Michael Jack-
son datasets. In the Iran dataset the number of tweets was in hundreds of thou-
sands and even with 5 tags co-occurrence it was still about 34K+ tweets. To
solve this we performed a random sampling from those resulting tweets to take
only 10% of them resulting in a smaller manageable dataset. The second problem
with the Michael Jackson dataset upon using 5 tags to decrease it to a manage-
able size we realized there were few unique domains for the embedded resources.
A closer look revealed this combination of tags was mostly border-line tweet
spam (MJ ringtones). To solve this we used only the two top tags “#michael”
and “#michaeljackson”, and then we randomly sampled 10% of the resulting
tweets to reach the desired dataset size (Table 2).
3.2 Egyptian Revolution Dataset
The one year anniversary of this event was the original motivation for this
study [13]. In this case, we started with an event and then tried to get so-
cial media content describing it. Despite its ubiquity, gathering social media for
a past event is surprisingly hard. We picked the Egyptian revolution due to the
role of the social media in curating and driving the incidents that led to the
resignation of the president. Several initiatives were commenced to collect and
curate the social media content during the revolution like R-sheif.org2which
specializes in social content analysis of the issues in the Arab world by using
aggregate data from Twitter and the Web. We are currently in the process of
obtaining the millions of records related to the Arab Spring of 2011. Meanwhile,
we decided to build our own dataset manually.
2http://www.r-shief.org/

6 Hany M. SalahEldeen and Michael L. Nelson
There are several sites that curate resources about the Egyptian Revolution
and we want to investigate as many of them as possible. At the same time,
we need to diversify our resources and the types of digital artifacts that are
embedded in them. Tweets, videos, images, embedded links, entire web pages
and books were included in our investigation. For the sake of consistency, we
limited our analysis to resources created within the period from the 20th of
January 2011 to the 1st of March 2011. In the next subsections we explain each
of the resources we utilized in our data acquisition in detail.
Storify: Storify is a website that enables users to create stories by creating
collections of URIs (e.g., Tweets, images, videos, links) and arrange them tem-
porally. These entries are posted by reference to their host websites. Thus, adding
content to Storify does not necessarily mean it is archived. If a user added a video
from YouTube and after a while the publisher of that video decided to remove it
from YouTube the user is left with a gap in their Storify entry. For this purpose
we gathered all the Storify entries that were created between 20th of January
2011 and the 1st of March 2011, resulting in 219 unique resources.
IAmJan25: Some entire websites were dedicated as a collection hub of media
to curate the revolution. Based on public contributions, those websites collect
different types of media, classify them, order them chronologically and publish
them to the public. We picked a website named IAmJan25.com, as an example
of these websites, to analyze and investigate. The administrators of the website
received selected videos and images for notable events and actions that happened
during the revolution. Those images and videos were selected by users as they
vouched for them to be of some importance and they send the resource’s URI to
the web site administrators. The website itself is divided into two collections: a
video collection and an image collection. The video collection had 2387 unique
URIs while the image collection had 3525 unique URIs.
Tweets From Tahrir: Several books were published in 2011 documenting the
revolution and the Arab Spring. To bridge the gap between books and digital
media we analyzed a book entitled Tweets from Tahrir [11] which was pub-
lished on April 21st, 2011. As the name states, this book tells a story formed by
tweets of people during the revolution and the clashes with the past regime. We
analyzed this book as a collection of tweets that had the luxury of a paperback
preservation and focused on the tweeted media, in this case images. The book
had a total of 1118 tweets having 23 unique images.
3.3 Syria Dataset
This dataset has been selected to represent a current (March 2012) event. Using
the Twitter search API, we followed the same pattern of data acquisition as
in section 3.1. We started with one hashtag, #Syria, and expanded it. Table 3

Losing My Revolution 7
show the tags produced from the tag expansion step. After that each of those
tags were input into a process utilizing the Twitter streaming API and produced
the first 1000 results matching each tag. From this set, we randomly sampled
10%. As a result, 1955 tweets were extracted each having one or more embedded
resources and tags from the expanded tags in Table 3.
Initial Hashtags Extracted Hashtags
‘Syria’ ‘Bashar’ ‘RiseDamascus’ ‘GenocideInSyria’ ‘STOPASSAD2012’ ‘AssadCrimes’ ‘Assad’
Table 3. Twitter #Tags generated for filtering the Syrian uprising
Table 4 shows the resources collected along with the top level domains that
those resources belong to for each event.
Event Top Domains (number of resources found)
MJ youtube (110), twitpic (45), latimes (43), cnn (30), amazon (30)
Iran youtube (385), twitpic (36), blogspot (30), roozonline (29)
H1N1 rhizalabs (676), reuters (17), google (16), flutrackers (16), calgaryherald (11)
Obama blogspot (16), nytimes (15), wordpress (12), youtube (11), cnn (10)
Egypt youtube (2414), cloudfront (2303), yfrog (1255), twitpic (114), imageshack.us (20)
Syria youtube (130), twitter (61), hostpic.biz (9), telegraph.co.uk (5)
Table 4. The top level domains found for each event ordered descendingly by the
number of resources.
4 Uniqueness and Existence
From the previous data gathering step we obtained six different datasets related
to six different historic events. For each event we extracted a list of URIs that
were shared in tweets or uploaded to sites like Storify or IAmJan25. To answer
the question of how much of the social media content is missing we test those
URIs for each dataset to eliminate URI aliases in which several URIs identify to
the same resource. Upon obtaining those unique URIs we examine how many of
which are still available on the live web and how many are available in public
web archives.
4.1 Uniqueness
Some URIs, especially those that appear in Twitter, may be aliases for the
same resource. For example “http://bit.ly/2EEjBl” and “http://goo.gl/2ViC”
both resolve to “http://www.cnn.com”. To solve this, we resolved all the URIs
following redirects to the final URI. The HTTP response of the last redirect has
a field called location that contains the original long URI of the resource. This
step reduced the total number of URIs in the six datasets from 21,625 to 11,051.
Table 5 shows the number of unique resources in every dataset.
4.2 Existence on the Live-Web
After obtaining the unique URIs from the previous step we resolve all of them and
classify them as Success or Failure. The Success class includes all the resources

8 Hany M. SalahEldeen and Michael L. Nelson
All Unique
2,293 1,187=51.77%
MJ Archived Not Archived
Available 316=26.62% 474=39.93%
Missing 90=7.58% 307=25.86% 397=33.45%
406=34.20% each/1,187
All Unique
3,429 1,340=39.08%
Iran Archived Not Archived
Available 415=30.97% 586=43.73%
Missing 101=7.54% 238=17.76% 339=25.30%
516=38.51% each/1,340
All Unique
5,517 1,645=29.82%
H1N1 Archived Not Archived
Available 595=36.17% 656=39.88%
Missing 98=5.96% 296=17.99% 394=23.95%
693=42.12% each/1,645
All Unique
1,118 370=33.09%
Obama Archived Not Archived
Available 143=38.65% 135=36.49%
Missing 33=8.92% 59=15.95% 92=24.86%
176=47.57% each/370
All Unique
7,313 6,154=84.15%
Egypt Archived Not Archived
Available 1,069=17.37% 4440=72.15%
Missing 173=2.81% 472=7.67% 645=10.48%
1242=20.18% each/6,154
All Unique
1,955 355=18.16%
Syria Archived Not Archived
Available 19=5.35% 311=87.61%
Missing 0=0% 25=7.04% 25=7.04%
19=5.35% each/355
Table 5. Percentages of unique resources from all the extracted ones we obtained per
event and the percentages of presence of those unique resources on live web and in
archives. All resources = 21,625, Unique resources = 11,051
that ultimately return a “200 OK” HTTP response. The Failure class includes
all the resources that return a “4XX” family response like: “404 Not Found”,
“403 Forbidden” and “410 Gone”, the “30X” redirect family while having infinite
loop redirects, and server errors with response “50X”. To avoid transient errors
we repeated the requests, on all datasets, several times for a week to resolve
those errors.
We also test for “Soft 404s”, which are pages that return “200 OK” response
code but are not a representation of the resource, using a technique based on a
heuristic for automatically discovering soft 404s from Bar-Yossef et al. [2]. We
also include no response from the server, as well as DNS timeouts, as failures.
Note that failure means that this resource is missing on the live web. Table 5
summarizes, for each dataset, the total percentages of the resources missing from
the live web and the number of missing resources divided by the total number
of unique resources.
4.3 Existence in the Archives
In the previous step we tested the existence of the unique list of URIs for each
event on the live web. Next, we evaluate how many URIs have been archived
in public web archives. To check those archives we utilize the Memento frame-
work. If there is a memento for the URI, we download its memento timemap and
analyze it. The timemap is a datestamp ordered list of all known archived ver-
sions (called “mementos”) of a URI. Next, we parse this timemap and extract

Losing My Revolution 9
the number of mementos that point to versions of the resource in the public
archives. We declare the resource to be archived if it has at least one memento.
This step was also repeated several times to avoid the transient states of the
archives before deeming a resource as unarchived. The results of this experiment
along with the archive coverage percentage are presented in Table 5.
5 Existence as a Function of Time
Inspecting the results from the previous steps suggests that the number of miss-
ing shared resources in social media corresponding to an event is directly propor-
tional with its age. To determine dates for each of the events this we extracted
all the creation dates from all the tweet-based datasets and sorted them. For
each event, we plotted a graph illustrating the number of tweets per day related
to that event as shown in figure 1. Since the dataset is separated temporally into
3 partitions, and in order to display all the events on one graph we reduced the
size of the x-axis by removing the time periods not covered in our study.
Fig. 1. URIs shared per day corresponding to each event and showing the two peaks
in the non-Syrian and non-Egyptian events
Upon examining the graph we found an interesting phenomena in the non-
Syrian and non-Egyptian events: each event has two peaks. Upon investigating
history timelines we came to conclusion that those peaks reflect a second wave of
social media interaction as a result of new incident within the same event after
a period of time. For example, in the H1N1 dataset, the first peak illustrates the
world-wide outbreak announcement while the second peak denotes the release
of the vaccine. In the Iran dataset, the first peak shows the peak of the elections
while the second peak pinpoints the Iranian trials. As for the MJ dataset the
first peak corresponds to his death and the second peak describes the rumors
that Michael Jackson died of unnatural causes and a possible homicide. For
the Obama dataset, the first peak reveals the announcement of his winning the
prize while the second peak presents the award-giving ceremony in Oslo. For
the Egyptian evolution, the resources are all within a small time slot of 2 weeks

10 Hany M. SalahEldeen and Michael L. Nelson
around the date 11th of February. As for the Syrian event, since the collection was
very recent there was no obvious peaks. Those peaks we examined will become
temporal centroids of the social content collections (the datasets). MJ (June
25th & July 10th 2009), Iran (June 13th & 1st August 2009), H1N1 (September
11th & 5th October 2009), and Obama (October 9th & December 10th 2009).
Egypt was (February 11th 2011) and the Syria dataset also had one centroid
on March 27th 2012. We split each event according to the two centroids in each
event accordingly. Figure 1 shows those peaks and Table 6 shows the missing
content and the archived content percentages corresponding to each centroid.
MJ Iran H1N1 Obama Egypt Syria
% Missing 36.24% 31.62% 26.98% 24.47% 23.49% 25.64% 24.59% 26.15% 10.48% 7.04%
% Archived 39.45% 30.78% 43.08% 36.26% 41.65% 43.87% 47.87% 46.15% 20.18% 5.35%
Table 6. The Split Dataset
Fig. 2. Percentage of content missing and archived for the events as a function of time.
Figure 2 shows the missing and archived values from Table 6 as a function of time
since shared. Equation 1 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 2 shows the modeled estimate for
the percentage of shared resources archived in a public archive.
Content Lost P ercentage = 0.02(Age in days)+4.20 (1)
Content Archived P ercentage = 0.04(Age in days)+6.74 (2)
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.

Losing My Revolution 11
6 Conclusions and Future work
We can conclude that there is a nearly linear relationship between time of shar-
ing 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.
7 Acknowledgments
This work was supported in part by the Library of Congress and NSF IIS-
1009392.
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