Open Journal of Political Science
2014. Vol.4, No.1, 8-15
Published Online January 2014 in SciRes (
The Political Domain Goes to Twitter:
Hashtags, Retweets and URLs
George Robert Boynton, James Cook, Kelly Daniels, Melissa Dawkins, Jory Kopish,
Maria Makar, William McDavid, Margaret Murphy, John Osmundson,
Taylor Steenblock, Anthony Sudarma wa n, Phili p Wi ese, Alparsian Zo ra
University of Iowa, Iowa City, USA
Received October 26th, 2013; revised November 30th, 2013; accepted December 11th, 2013
Copyright © 2014 George Robert Boynton et al. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited. In accordance of the Creative Commons Attribution License all
Copyrights © 2014 are reserved for SCIRP and th e owner of the in tellectual property Georg e Robert Boynton et
al. All Copyright © 2014 are guarded by law and by SCIRP as a guardian.
The argument is twofold. One, the character of political communication on Twitter is sufficiently differ-
ent from the general character of the Twitter stream, from the “firehose”, as it is known that political
communication should be considered as a separable domain of communication. Specifically, retweets,
urls, and hashtags are used far more frequently in political communication than is true for the full stream
of messages and that reflects communication which is more interactive than is generally the case. Two,
context is needed for characterizing twitter streams. In the case of political communication there are pa-
rameters on the use of these tools, which facilitate interactive communication that sets such a context. If
most political tweets have retweets or urls between some low bound and a high bound, then one has a way
to characterize any specific stream that is being investigated. The analyses will begin the investigation of
these parameters.
Keywords: Political Domain; Twitter; Has hta gs ; Retweets; URLs
The argument is twofold. One, we argue that the character of
political communication on Twitter is sufficiently different
from the general character of the Twitter stream, from the
“firehose”, as it is known that political communication should
be considered as a separable domain of communication. Spe-
cifically we will show that retweets, urls, and hashtags are used
far more frequently in political communication than is true for
the full stream of messages and that reflects communication
which is more interactive than is generally the case. This means
generalizations or relationships found in the broad stream may
not be relevant to political communication and vice versa. Two,
we argue that context is needed for characterizing twitter
streams. In the case of political communication there are para-
meters on the use of these tools, which facilitate interactive
communication that set s such a conte xt. If mos t politic al tweet s
have retweets or urls between some low bound and a high
bound, then one has a way to characterize any specific stream
that is being investigated. This context is important in inter-
preting the importance of the number of retweets in a protest
situation or the number of urls in campaign communication, for
example. Our analyses will begin the investigation of these
We first review the historical development of Twitter and the
tools for communication that were first imagined and put to use
by Twitter users. That is followed by a brief review of the rele-
vant research and a characterization of the methods used in the
research reported here. The primary focus is an examination of
streams of political communication in 2009-10, in 2011, and
2012. The research reported was chosen to provide a very broad
view of political communication. Streams of messages or vary-
ing size and across many topics are analyzed.
The Development of Twitter as a Means
of Communication
Twitter was launched in March of 2006 and has, along with
other social media, seen phenomenal growth since. By March
2008 1.3 million people had signed on as users. But it was in
2009 that Twitter broke into the general culture. It grew from 6
million in April of 2009 to 105 million in April 2010, and that
extraordinary growth has continued. (Buck, 9/20/2011) In 2012
Twitter led all social media growing 40% during the year.
(Bennett, 1/28/2013) By its seventh anniversary in 2013 there
were more than 200 million active users and more than 400
million messages a day. (Moscaritolo, 3/21/2013)
When Twitter was launched it was a simple broadcast and
subscribe service. One wrote up to 140 characters, posted the
message to Twitter, and the message was then available to users
who followed you. Users quickly invented practices and tech-
nology that would enrich communication beyond the simple
broadcast-subscribe model. There was no procedure for ad-
dressing another user or for being addressed. Very early, in
2006, the @username practice was adopted to bring identity
into the communication stream. If you wanted to address other
users @username was the way of identifying them. That was
followed by retweets (Helmond, 1/19/2013), hashtags (Stadd,
11/27/2012), searching via the Twitter APIs, and shortened urls
developed by users and that were quickly adopted in Twitter
communication. When Twitter was preparing to formalize ret-
weeting they acknowledged the importance of the inventions of
their users.
Some of Twitter’s best features are emergent—people in-
venting simple but creative ways to share, discover, and com-
municate. One such convention is retweeting. (Stone, 9/13/
These emergent features have been important in the devel-
opment of Twitter as a medium of communication. Twitter, like
many of the social media organizations, has not been particu-
larly forthcoming about numbers of users and other features
being used. But there is a considerable group of publications
that supply information on its growth. The same is not true for
the incidence of use of the features invented by its users. That
they are being used is well known. How much they are being
used is much more difficult to determine. One focus of this
paper is on the use of these features beginning with 2009 and
running through 2012.
Previous Research
The research on Twitter communication is quite substantial.
In particular, scholars in computer science have been actively
researching the use of Twitter from as early as 2008 and 2009.
But much of this work is based on an implicit assumption that
Twitter communication is an undifferentiated field. There has
been little research examining domains of communication with-
in the Twitter stream in which communication may be syste-
matically different than it is in other domains. A primary focus
of this paper is examining the domain of political communica-
tion using Twitter. The goal is to move beyond specific in-
stances of politics using Twitter to broadly characterize a do-
main of communication in which retweets and urls and hash-
tags are used differently than they are beyond this domain. We
want to show that their use differentiates this as a separable
field of communication within the broader stream of Twitter
A widely cited early study of the mode of communication fa-
cilitated by the features invented by Twitter users was Tweet,
Tweet, Retweet: Conversational Aspects of Retweeting on
Twitter.” (Boyd, Golder, & Lotan, 2010) Retweeting is im-
portant because it moves the communication beyond broad-
cast-subscribe to interaction. Every retweet is a tweet that was
written by someone other than the person retweeting, read by
the person retweeting, and the retweet was the n available t o the
followers of the person retweeting. Retweeting is three “par-
tiesin communication. For their research they collected a
sample of 725,000 messages during the spring of 2009. They
found that 3% of the tweets were retweets, 5% included a
hashtag, and 22% contained a url. During July of 2009 Vik
Singh collected a sample of 10 million tweets. (Singh, 10/12/
2009) He found that 4% were retweets, 1% included a hashtag,
and 18% included urls. The two seem similar enough to suggest
this is how the three practices were being used in messages in
The Boyd, Golder and Lotan paper was widely cited; Google
Scholar reports 360 citations to the paper. However, it did not
initiate a robust stream of research. There have been few papers
subsequently reporting population numbers for retweets, urls
and hashtags. The additional baseline numbers we have found
include a 2010 study by Sysomos, a new media analytics firm,
which collected a sample of 1.2 billion tweets during August
and September and found that 6% of tweets included a retweet.
(Evans, 9/30/2010) In September of 2011 a sample of 5.6 mil-
lion tweets was collected at the University of Iowa. Thirteen
percent were retweets, 13% contained a url, and 16% contained
a hashtag. In 2012 Leetaru, et al collected a 10% sample of the
Twitter stream for one month. In their sample 23% were ret-
weets and 14.6% contained a url. (Leetaru, et al 5/2013). They
also report that only 7.8% of the urls they found referenced
mainstream English-language news. These set baseline num-
bers that can be used to compare with the collections of politics
on Twitter used in this analysis.
There have been many studies of politics on Twitter. The
Pew Research Center produces a running tally of new media
use including a daily report on the percentage of people in the
United States who have a Twitter account (Pew Research Cen-
ter, ongoing). Elections have often been the site for research.
An early study was Predicting Elections with Twitter: What
140 Characters Reveal about Political Sentiment(Tumasjan,
Sprenger, Sandner, & Welpe, 2010) And there have been a
number of reports about elections since. Anstead and
O’Loughlin conducted a study of messages posted to Twitter
during the question and answer period of a popular British TV
political talk show. (Anstead & O’Loughlin, 2011) They were
able to trace minute by minute responses to the discussion on
the TV show. These were early studies of Twitter and political
communication, and they were followed by many comparable
studies. But these and other studies focus largely on individual
cases. There have been almost no comparative studies. One
exception to this generalization is Bruns and Stieglitz, Quan-
titative approaches to comparing communication patterns on
Twitter.(2012) But this is clearly the exception when com-
pared with other studies of Twitter and politics.
This report is about politics on Twitter. The intention is to
describe a domain of communication to show how it is different
from the overall stream of communication. It also examines
variation within the streams of messages about politics. The
primary focus is on the use of retweets and urls in the tweets.
Both are important because they are sharing or conversation as
Boyd, Golder and Lotan noted. Retweeting is sharing tweets
one has read with one’s followers. Urls are important because
they are a way of bringing communication from outside Twitter
into the stream and sharing that communication. One of the
standard characterizations of Twitter communication is that it is
simply expressing one’s thoughts with no audience in mind. It
is not communication/interaction, but is individual broadcasting
their thoughts instead. If retweeting and the inclusion of urls are
high compared to the overall stream then one can conclude this
sets the domain apart from the overall stream by being much
more conversational.
The report is based on a large number of collections of Twit-
ter messages beginning in 2009 and running through 2012.
Every data set was collected using Archivist, which is a Win-
dows desktop computer program that was running continuously.
It accessed the Twitter search API at five minute intervals.
Since Twitter would respond with only 1,500 tweets per request
that set an upper limit on the collection. However, it could col-
lect up to 18,000 per hour or 432,000 per day running 24 hours
a day. The limit of 18,000 per hour was exceeded only on very
special occasions such as important speeches in political con-
ventions when interest was particularly high. Twitter does not
reveal how much of the total stream is available through the
search API. However, in the spring of 2012 the number of
messages collected searching for Obamawas approximately
200,000 a day using Archivist and that was compared with the
number in the Gnip stream that was also approximately
200,000 a day. Since 200,000 a day was far more than the reg-
ular flow in any other stream it seems this is a reasonable
record of the messages being posted to Twitter for these collec-
The searc h term is a key element in t he quality of the coll ec-
tion. Some search terms were obvious. Obamawas over-
whelmingly how people referred to the president of the United
States in their tweets. However, “barackobama”, which was the
username of the Obama Twitter account, was used in about
one-fifth as many tweets as mentioned Obama, and there was
very lit tle overlap between the two. So both were collected. The
Occupy Wall Street tweets started with day of rage, that
evolved into #occupywallstreet, and that evolved into #ows,
and then it became #occupy[name of town] as the movement
spread from one location to another. Tracking changes like that
was an important concern in the collections. In collecting
tweets about a subject one has to discover how they are being
referred to by Twitter users. It requires an exploratory process,
and given the variety of expressions possible it is clear that
some are missed because they are not found using the search
term or terms used for collecting. For the 125 collections of
2009 and 2010 there is a document describing the construction
of each research term (
The analysis is based on a very large number of collections.
There are 125 in 2009 and the first part of 2010, for example.
One might say that a sample of political messages on Twitter
would have been a better way to conduct the search. But it is
not possible to sample political messages. There is no way to
define the population in such a way that one can draw a sample.
One could draw samples for any of the streams of messages
collected and used in the analysis, but that would not be a sam-
ple of all political messages. Imagine trying to define a popula-
tion that includes all of the political issues that might be
tweeted about at any point in time. That is not a feasible strate-
gy. The next best strategy seemed to be collecting an over-
whelming number of streams that were politically relevant for
analysis, and that is the strategy employed in this research.
The collections range from a few days to collections that
continued for two or more years. The analytic strategy used
varies with the type of collection being examined.
The Beginning: 2009-2010
As already noted Twitter experienced phenomenal growth in
2009. It was a 17 fold growth from 6 million members to 105
million. As impressive as its 2012 growth of 40% was, which
led all social media organizations, 2012 was almost nothing
compared with the growth rate from 2009 to 2010. Even as the
number of users grew phenomenally so did the number of mes-
sages being posted to Twitter. Early in 2010 the number of
tweets per day reached 50 million. (Parr, 2/22/2010) That was
up from 300,000 a day in 2008 to 35 million by the end of 2009
and then reaching 50 million only two months later. Twitter had
hit the big time. And that makes 2009 a good point at which to
begin this analysis.
This initial analysis includes the 125 studies that were started
beginning in July of 2009 and running through March of 2010.
It is a very heterogeneous set of collections. It begins with
#HC09 which was the Obama administration’s call to support
his health care reform legislation. It includes collections about
American politics with long running political concerns such as
the health care reform and the news of the day such as the day
Barney Frank made news with his response to a question in a
town meeting. It includes international politics such as a collec-
tion about Iran’s agreement to accept IAEA nuclear inspections.
It is too diverse a set to be adequately described here, but in-
formation about the collections is available online at There is a page describing
each search, including the exploration to develop search terms,
the length of the search and the number of tweets captured.
There is also a data file in tab delimited form there.
How long did a stream last? That is, of course, dependent on
the researcher as well as the messaging activity. In general the
collecting continued until there were only a few tweets a day,
but there were streams for which that did not happen. “Terror-
ismis a stream of messages that is very unlikely to go away
for the foreseeable future. And one might only want to know
about a specific periodthe day of the State of the Union ad-
dress, for example. With the caveat that there were about ten
streams for which collection had not stopped, in this set the
streams lasted an average of 63 days with a standard deviation
of 63. This and many of the distributions are very skewed, and
the mean and standard deviation or a figure are not a very good
indication of the distribution. So the distribution is divided into
quintiles and is given in Table 1.
The 25 streams ending most quickly lasted between 1 and 12
days. The top fifth lasted between 136 and 244 days with ten
continuing beyond the point of this analysis. A few ended in
only a few days, but most of the streams had staying power.
The total number of messages in a stream varied widely. The
stream with the smallest number of messages was hack baidu,
which was a stream of 35 messages about the controversy be-
tween Google and China. A very few people thought it would
be funny to have hacking turned back on Baidu, which is the
leading Chinese search engine. As is obvious, it did not take off.
The stream with the largest number of messages was #hcr with
a total of 586,382 messages. The distribution was very skewed.
The mean message per stream was 31,218, and the standard
deviation was 70,246. When the standard deviation is twice the
mean is a very skewed distribution.
Dividing the streams into quintiles makes the same story, but
gives more detail about the distribution in Table 2.
Almost four-fifths are below the mean, and the top fifth goes
to gigantic streams. At least they were gigantic streams in this
time period.
Boyd, Golder, and Lotan found that the tweets in their sam-
ple included
5% of tweets contain a hashtag (#) with 41% of these also
containing a URL;
22% of tweets include a URL (http:”);
3% of tweets are likely to be retweets in that they contain
“RT”, retweet” and/or “via” (88% include “RT”, 11% in-
clude “via” and 5% include retweet”).
Table 1.
Streams lasting number of days in quintiles.
Quintiles 1 2 3 4 5
Days 1 - 12 13 - 23 24 - 43 44 - 135 136 - 244
Table 2.
Total messages per stream by quintile.
1 2 3 4 5
35 - 1.3k 1.3k - 3.1k 3.3k - 8.6k 9.1k - 33.9k 44.7k - 586.4k
The number of hashtags for the streams in this set is not eas-
ily averaged. Twenty-three of the streams were found by
searching for a hashtag. #hcr, for example, is a stream of mes-
sages. There are 585,000+ messages and every one of them
contains the hashtag. The same is true of #Palin, #teaparty,
#welovethenhs, #cop15, and others. If you look at only the
streams that are not identified by containing a hashtag the range
is from 1% of the messages that were a response to the death of
Senator Ted Kennedy to 79% for messages about an Iranian
protest in November 2009. The Iranian protest in February
2010 was next highest with 78% containing a hashtag. The
mean for the 102 not identified by a hashtag is 19.7% and the
standard deviation is 12.5%. Including all 125 streams and
dividing into quintiles gives the distribution in Table 3.
The results displayed i n Table 3 for these collections is very
different from the general sample. The range is from 1% to
100%. Eighty percent of the studies have a higher percentage of
tweets that include hashtags than was found in the general sam-
ple. The top twenty percent of the collections have between
77% and 100%.
We should understand the hashtag as generally identifying an
audience with whom the writer wants to communicate. When
someone adds #cop15 to their message that seems unlikely to
be an after thought. It is a way of entering into a stream of
communication that is well known and well practiced. #cop15
was a specific meeting of nations to make plans for saving the
global environment. But hashtags are also used as name of
groups as in #teaparty or #p2, which is a designation for pro-
gressives. When they are added to a message it does not so
much indicate what the message is about as who might be in-
terested in this message. So local meetings of teaparty organi-
zations can be advertised to people who are interested by using
the #teaparty hashtag. Hashtags are not the only way to consti-
tute a stream of messages, but for this set they seem to be an
unusually important element in constituting the stream.
Urls function as important extenders of the message. They
are almost always used either to say did you see that” where
the “that is in the document specified with the url or they are
used as evidence for justifying a claim where the evidence is in
the document specified with the url. In both cases they point the
reader beyond the tweet. They connect the message to the po-
litical world outside of Twitter.
For these streams the percentage of messages containing a
url, http://, ranges from 29% to 98%. The mean for all 125
streams is 69% and the standard deviation is 16.7%. When
divided into quintiles in Table 4.
This is very different from the Boyd, et al finding. In their
sample only 22% of the tweets contained urls. The political
streams, shown in Table 4, are out on the fringe of the distribu-
Table 3.
Percentage per stream containing hashtag by quintile.
1 2 3 4 5
1% - 13% 13% - 16% 17% - 23% 23% - 58% 77% - 100%
Table 4.
Percentage messages per stream http:// by quintile.
1 2 3 4 5
29% - 51% 51% - 67% 67% - 75% 75% - 84% 84% - 98%
tion for all Twitter messages. The collection with the smallest
percentage of urls has a larger percentage than the percentage
found in the sample of the entire Twitter stream. Political
streams of messages are about politics. Much of the rest of
Twitter is about the self. The standard claim about Twitter is
that most messages are as trivial as what one had for breakfast
or what town you are driving through. They are not trivial to
the individual and, perhaps, a close circle of friends. But they
are not about public affairs in the same way the political
streams are. The large difference need not be surprising, of
course. The messages were chosen because they were about
public affairs. That they use the url to point to public docu-
ments seems that it might be expected. It does, however, mark
off these messages from the mainstream” of Twitter messag-
Retweeting is quoting another twitter message. It is usually
done by starting the message with RT @[name of original
author] original message”. At times the @[name] is left off,
which is why the Microsoft researchers have a rather elaborate
description about how they searched. What is the point? It is a
continuation of the pass it alongsyndrome. The person saw it,
liked it, and wanted to pass it along to followers and anyone
else who might come across it. It is about circulating ideas
through the network, and technology blogs have thought it im-
portant as the mechanism for going viral, which they think of as
The Microsoft researchers found that 3% of their sample in-
cluded retweets. The range for the streams about politics is
from 4% to 72%. The mean is 37.5% and the standard deviation
is 13%. When divided into quintiles in Table 5.
While retweeting is not as prevalent in these streams as is
using urls the incidence of retweeting is much higher than
found in the sample drawn by the Microsoft searchers.
These results for retweeting emphasizes the point about using
hashtags and urls. Twitter is used in political messaging as a
public domain in which individuals are sharing what they know
and what they think about public affairs. These streams are
public affairs. Twitter becomes an enlargement of the public
domain. Just as the media corporations must move over in the
face of new streams of news so the argument in the public do-
main is expanded by microblogging. By 2013 this had become
clear and Costolo, the CEO of Twitter, and the Brookings In-
stitution were using global town squareas the way to charac-
terize communication on Twitter (Brookings, 6/26/2013).
Arab spring, the campaign for the Republican nomination for
president, and Occupy Wall Street all occurred in 2011. They
Table 5.
Percentage retweets per stream by quintile.
1 2 3 4 5
4% - 27% 27% - 34% 34% - 40% 40% - 47% 47% - 72%
were major public events, and Twitter was used extensively in
all three. Instead of examining a conglomerate of collections for
2011 these three are the focus of the analysis.
Arab Spring: First Tunisia, then Egypt, and Bahrain, and
Libya, and Syria and finally Yemenrevolution swept across
the North African nations in the spring of 2011. Four revolts
became a change in the leadership of the nation, and two, Ba-
hrain and Syria, continue for at least two more years. Social
media played an important role in the revolutions as a means of
giving impetus to the local protests and appealing to the world
for support. In communication via Twitter hashtags were used
to identify messages about the revolts. For Bahrain February 14
was to be the day the protests would begin, and for many
months the hashtag used to identify tweets was #feb14. In
Libya and Syria the hashtags were constructions of the names
of the nations: #Libya and #Syria.
For Bahrain, Libya, and Syria the hashtags were the search
terms used collecting tweets that referred to the revolt. It was
how they were identifying their messages so they were the ap-
propriate search terms. The collections began simultaneously
with the beginning of the protests. In Bahrain that was February
15. In Libya the collection began at the end of February, and
the collection began on March 15 in Syria. The results pre-
sented here are for collections running through the first of June
The number of tweets found for the three searches are sub-
stantial. In Bahrain, which has the smallest population, the
number of tweets collected was 738,136. Libya and Syria both
had just over two million messages posted to Twitter during the
spring. For Libya it was 2,147,624 and for Syria 2,071,351. The
average numbers of messages per week were: 52,385 for Ba-
hrain, 150,346 for Libya, and 188,304 for Syria.
Since hashtags were used in the search terms all of the tweets
contained a hashtag. Retweets and urls are shown in Table 6.
The means are computed from the percentages with retweets
and urls each week. For the entire spring Bahrain had the high-
est percent of tweets including a retweet with 70.2%. Libya is
59.6% and Syria is 56.0% as seen in Table 6. In each case the
percentage of tweets including a retweet is substantially higher
than the percentage containing a url. In all three cases the per-
centage of tweets with a url is in the low forties.
The other point to note is the extent to which these are much
greater than in the total stream of Twitter messages. The small
sample available for 2011 had 13% with retweets and 13% with
urls. As in the collections of 2009-2010 the political streams are
much more interactive than is the total stream.
Republican campaign: Candidates arrived in Iowa in January
2011, though some had been in Iowa even earlier, and the
campaign started. It ran through the next January when Romney
was the last man standing. There were two constants in the race:
Romney was the consistent leader and Ron Paul was a consis-
tent second, but everyone agreed he would never make it to
number one. And there was a string of challenger whose surge
and decline was much of the news of the campaign and much of
the communication on Twitter. Bachman was the first challen-
Table 6.
Retweets and Urls in Twitter messages.
@RT Urls
Mean Std Dev Mean Std Dev
Bahrain 70.2% 2.7% 41.4% 6.5%
Libya 59.6% 2.3% 44.1% 7.3%
Syria 56.0% 5.4% 40.1% 8.5%
ger. When she declined Perry rose to challenge. His campaign
crashed more than declined. Perry was followed by Herman
Cain whose campaign suffered the same fate. Gingrich was
next, but his challenge was shortlived. And the final challenger
was Santorum. When his campaign declined there was no one
left, and Romney was the winner.
The total number of messages posted to Twitter about the
candidates was 21,549,866; see Table 7. Romney was men-
tioned in the largest number of tweets at 11,540,806, or 53.6
percent. Next was Ron Paul receiving 2,328,934 (10.8 percent),
Bachman with 2,005,351 (9.3 percent), Perry with 1,598,999
(7.4 percent), Cain with 1,514,739 (7 percent), Gingrich with
1,470,599 (6.8 percent), and Santorum with 1,090,438 (5.1
percent). Excluding Romney, all of the candidates fell between
5 to 10 percent of the tweets.
Hashtags were not necessary when posting a message to
Twitter about the candidates. The names of the candidates were
well known, and in 2009 Twitter had added a procedure to ve-
rify accounts that kept the potential confusion about who was
the “correct” Romney or Santorum to a minimum. (Cashmore,
6/11/2009) Hashtags appeared only in the upper twenty percent
of the tweets mentioning the candidates with the excepton of
Santorum where they were in 33.92% of the tweets. Retweets
were the second most frequently used of the three practices.
The percentage of messages including a retweet ranged from a
low of 34.3% for Santorum to 42.53% for Perry. For five of the
seven candidates the percentage of retweets was very close to
40%. Referring to documents with urls was the most frequently
used of the practices. The percentage of messages containing a
url ranged from 60.97% for Gingrich to 35.48% for Santorum.
Even though just over half of the messages mentioning one of
the candidates mentioned Romney the use of hashtags, retweets,
and urls is consistent with messages mentioning other candi-
dates with 29% hashtags, 39% retweets and 49% urls. Only the
tweets mentioning Santorum deviate from this general pattern
by the three being roughly equally included in the messages.
Three features of the collections are noteworthy. First, they
are very large collections; the patterns are quite stable. Second,
the numbers for hashtags, retweets, and urls are at least twice as
large as for the general Twitter stream. The pattern of commu-
nication is much more interactive than is generally the case.
Third, the relative ranking of retweets and urls is not the same
as was true for the Arab spring collections. The percentage of
messages including a url is greater than the percentage includ-
ing a retweet, and that is just the reverse of the relationship in
the Arab spring collections where there were more retweets and
fewer urls.
Occupy Wall Street: The first public protests were the day
of rage, which was a protest on September 11, 2011. The
stream of messages evolved into #occupywallstreet as the day,
Table 7.
The campaign for the Republican nomination.
Candidate Tweets Hashtags Retweets Urls
Romney 11,540,806 29.3% 39.0% 49.1%
Ron Paul 2,328,934 30.1% 35.8% 45.8%
Bachman 2,005,351 29.6% 40.6% 50.8%
Perry 1,598,999 26.8% 42.5% 55.1%
Cain 1,514,739 26.5% 41.8% 43.6%
Gingrich 1,470,599 29.3% 38.8% 60.9%
Santorum 1,090,438 33.9% 34.3% 35.5%
September 11, passed. On October 1, 2011 #occupywallstreet
became a global rallying cry. October 1 was the day they
marched across Brooklyn Bridge, were arrested in large num-
bers, and tweets using #occupywallstreet jumped from 55,000
on September 29 and 73,000 on September 30 to 150,000 on
October 1. On October 6 the rallying cry evolved once again.
The 140 character limit was too much of a challenge for #oc-
cupywallstreet. The word went out that #OWS should be used
instead. #occupywallstreet did not disappear, but it became a
much less frequently used hashtag. The occupy movement
broadened as it became a local global movement. #occupy [city
name] was added as groups of people all over the world rose to
challenge the status quo. Tracking all of the variants became
very difficult. The first weeks were a hea dy time. Camps
were set up as spots across the globe were occupied to express
concern. Challenges were faced. Police in many of the cities
challenged the encampments with all of the force they could
bring to bear. The news media focused on the conflict. The
occupy movement was big news. And it was big on Twitter as
well. Twitter was the locus of its rallying cry.
The first month of the energized movement saw a remarkable
outpouring of messages on Twitter using either #occupy-
wallstreet or #ows. The total was 3,743,144 or 124,771 occupy
messages a day. Not all were favorable, of course. But this
reflected great attention to the movement that was sweeping
across the globe. As in the Arab spring messages all of the
messages included a hashtag as its defining characteristic. Sixty
percent of the messages were retweets. This was a stream of
extreme sharing. The percent of messages containing a url was
As with the other collections this one has more than twice as
many retweets and urls as in the global stream of Twitter mes-
sages. Another pattern emerges with these comparisons, how-
ever. In revolutionary times retweets outweigh urls. Both are
sharing, but retweets are sharing sensibilities. They share a con-
struction of the situation. They share a characterization of the
enemy. They share joy and agony. Urls can participate in that
type of sharing by pointing to blog posts, photos and videos.
But the evocative expression of sensibility is retweeted at a
much higher volume than in more standard political situations
such as an election.
The pattern of retweeting occurring more than including urls
or vice versa is not limited to these two revolutionary situations.
In 2013 at almost the same date a revolutionary protest was
occurring in Turkey, and the world was discovering that the
United States was collecting a horde of electronic information
about every person in the world using electronic communica-
tion. The comparison is eleven days of protest in Turkey from
June 1 through June 11 and eleven days of reaction to the in-
formation Snowden was releasing and was being published by
The Guardian from June 25 through July 5. In eleven days
3,017,508 tweets were collected addressing the Turkish protest
for an average of 274,318 per day. The search accessed the
Twitter streaming API so this is only a sample of the tweets
that were posted to Twitter.
Table 8 gives the number of tweets that contained a retweet
and a url. For the Turkish protest collection 69.2% of the tweets
contained a retweet and 41.0% contained a url. The collection
of twitter messages mentioning either Snowden or NSA has 1.5
million tweets in eleven days. This was also a search using the
streaming API and thus is a sample. In this case the percentage
of the messages containing a retweet was 46.5% and the per-
centage containing a url was 60.7%. These were two controver-
sial events that drew a high level of messaging as people ex-
pressed their sensibilties concerning the events. Turkey is a
localprotest that encountered strong police opposition mov-
ing it to revolution. While people might be dismayed by what
was learned from the Snowden releases they did not engage in
revolution. And consistent with the difference in the situations
retweets are much higher in the revolutionary situation, as was
true for Arab spring and the occupy movement. And urls are
more prominent in the tweets about what is being learned from
the Snowden releases as was true for the Republican campaign.
2012 was election year, but it began as does every year with
the President delivering the State of the Union address to Con-
gress. According to Twitter 766,681 messages were posted dur-
ing the President’s address. (Twitter Blog, 1/24/2012) Looking
at the messages posted before, during and after reveals another
pattern that is important in characterizing the political domain.
Messages were being posted to Twitter at a much higher
speed than could be captured. The upper limit for an hour was
18,000 given a search every five minutes. So this report is
based on a small sample of tweets that were captured by
searching for two hours before the speech, during the speech,
and for two hours after the speech.
The Obama administration had pushed very hard for using
#SOTU in messages posted to Twitter about the address. They
were successful as shown in Table 9. The percentage of tweets
containing hashtags was extremely high. However, it is the
pattern of interaction that is most noteworthy. Retweeting is
interaction within the stream. Every retweet is a tweet that was
read and then shared with followers. So 45.6%, 41.2% and
59.6% of the messages started with reading the message being
retweeted. Retweeting is down slightly during the address as
they watched the president. Then it springs up to 60% after the
address when they are giving their reactions to what the presi-
dent has said and what others are saying about the speech. The
pattern is the reverse for urls. First, there are many fewer of
them; 27% before, 5.8% during, and 17.2% after. References to
external sources are few in number, and they go almost to zero
during the address. During the address they are concentrating
on the president and other persons who are tweeting. And after
the event they do not turn to external sources for cues to share.
Instead retweeting, communication within the stream, goes up
Table 8.
Two streams in 2012.
Total Tweets @ RT Urls
Turkey Prote st 3,017,508 2,089,475 1,238,193
69.2% 41.0%
Snowden 1,504,052 698,396 913,172
46.4% 60.7%
Table 9.
Twitter and the 2012 State of Union Address.
Total Hashtags @ RT Urls
Before 30,349 83.4% 45.6% 27.0%
During 16,761 97.2% 41.2% 5.8%
After 30,854 91.7% 59.6% 17.2%
significantly, and bringing in external sources only goes up to
17.2% of the tweets.
What this shows is communication that is very largely con-
tained within the stream of Twitter messages. They are concen-
trating on the president, but their communication is with others
who are communicating about the event. The standard news
media play a very modest role when Twitter users are focused
on an event like the State of the Union address.
There were four presidential debates. Debates 1, 3, and 4
were between the candidates for the presidency. Debate 2 was
between the vice presidential candidates. The totals are very
different because three different sampling procedures were used.
But each is a small sample of the total messages posted to
The pattern in these debates is very similar to the pattern
during the State of the Union address.
The point to notice in Table 10 is the focus of communica-
tion during the debates. Half of the messages are retweets, and
only 4.6% to 7.3% are references to outside sources of com-
ment. Half of the messages start with reading the message that
is being retweeted. It is a domain of communication with a very
high level of internal interaction.
The goal of the paper has been to show that political com-
munication on Twitter is a domain that is differentiable from
the main Twitter stream. If that case can be made, then an im-
portant result that based on collections from the total stream
would not necessarily be generalizable t o political communica-
tion. The domain of political communication would require
research specifically designed for it.
In addition, characterization of the domain would provide a
context for interpreting specific studies about politics on Twit-
For example, if 30% of the tweets in a collection contained a
retweet or contained a url then would that be interpreted as
many or few? Clearly it would not be few by the standard of the
total Twitter stream, but it might well be characterized as small
in terms of politics as a domain of communication. The collec-
tions summarized here become a baseline against which the
Table 10.
Twitter and the Presidential Debates of 2012.
Total Hashtags RT @ Urls
Debate 1 195,669 59.5% 50.4% 7.3%
Debate 2 337,355 38.0% 49.1% 6.5%
Debate 3 329,775 34.6% 50.3% 4.6%
Debate 4 1,978,939 41.8% 59.6% 6.0%
results of any specific study can be assessed.
The focus of the report has been on hashtags, retweets, and
urls. These were inventions of the users to facilitate communi-
cation. But these are not the only practices that might be inves-
tigated. One could examine the number of followers for persons
participating in the political domain compared with the total
population of Twitter users. One might investigate density of
the network produced by linking in the follower relationship.
And there are many other subjects to be investigated that are
not covered here that would enrich the characterization of the
domain. If our interpretation is appropriate, then this is a se-
parable domain and it is important to characterize it as such.
The collections examined here demonstrate much greater use
of hashtags, retweets, and urls in the political domain than what
is true for the total stream of Twitter messages. Every collec-
tion fits this pattern. The interpretation of that finding is that
there is much more communication as interaction rather than
simply broadcast in the political use of Twitter. Hashtags are an
invitation to communication. They are the online version of a
meeting site. If you want to communicate about a subject this is
where that communication is going on. Retweeting is an indica-
tion of readi ng in the domain. Every retweet i s a tweet that was
read before it was retweeted. When forty to sixty percent of the
messages are retweets, this means great readi ng as wel l as great
writing. Urls bring communication external to Twitter into the
stream. In this move Twitter communication is integrated into
the broader stream of political messages. And when those ex-
ternal communications begin to refer to communication on
Twitter, this integrates the stream from the other direction.
Twitter communication is not isolated from the broader stream
of political communication when urls are widely used.
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