Open Journal of Political Science
2013. Vol.3, No.3, 91-97
Published Online July 2013 in SciRes (
Copyright © 2013 SciRes. 91
The Reach of Politics via Twitter—Can That Be Real?
George Robert Boynton, Andrew Bates, Edward Bettis, Matthew Bopes, Richard Brandt,
Derek Fohrman, Jeremy Hahn, Tressa Hart, Caleb Headley, Jory Kopish, Robert Maharry,
Joseph Matson, Kierstin Mohoff, Rose Mraz, Matthew Palmer, Laura Pena,
Brittany Phillips, Anne Rhodes, Hanna Rosman, Clint Sievers, Daniel Tate, Sean Tyrrell,
Javin Villarreal, Philip Wiese, Alden Wignall
Department of Political Science, The University of Iowa, Iowa City, USA
tressa-hart@uiow a .edu,, jory-k opish, robert,, kierstin-m oh,,,,,,, clint-si,,,,,
Received December 8th, 2012; revised May 2nd, 2013; accepted May 21st, 2013
Copyright © 2013 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 origina l w o rk is properly cited.
If the reach of communication via Twitter is as great as it seems to be, that means a remarkable recon-
struction of the public domain is in process. Until recently the public domain was the mass media and
what they presented to their audiences. Now the audience has voice and is audience only in a new way.
The potential consequences for the organization of politics are great. But there is the “if”. The report ex-
amines two challenges to the claim that the reach of the new media is as great as it may appear to be. One
challenge is the number of individual accounts on Twitter. There seem to be too few to have the reach
suggested. The other challenge is fake accounts. If the number of fake accounts is great, then the numbers
for reach are a gross exaggeration. Those two challenges are examined and shown to be appropriate, but
that the end result is diminishing the numbers about reach much less than would question the reconstruc-
tion of the public domain
Keywords: Twitter Followers Reach
We know that Twitter has become an important venue for
political communication. For example, the first US presidential
debate of 2012 produced 10.3 million messages posted to
Twitter (Twitter Blog, 2012). That was 77% of the online
communication about the debate (Fitzpatrick, 2012). And there
were 31 million messages on Election Day (Sharp, 2012). Not
only is it the venue of choice for political communication, it is
also a particularly good venue for trolling for campaign contri-
butions. Twitter users who see political ads are 97% more
likely to visit a campaign donation page than those who do not
see an ad (Dugan, 2012). If that is not enough, there is also the
two-step flow of communication called following.
Three numbers set the st a g e.
During ten days of March 2012, Twitter messages posted by
ThinkProgress could be viewed 47 million times (Boynton,
2012e). That was 4.7 million potential views a day. Compare
4.7 million to 2.8 million, which is the audience for The
O'Reilly Factor on Fox News, and 2.3 million, which is the
total viewers of The Daily Show of Jon Stewart—the two TV
persons with the largest audiences.
At the Republican Convention Clint Eastwood spoke ad-
dressing most of his remarks to an empty chair; the chair from
which the president was missing. That was followed by the
Obama campaign’s Twitter quip “This seat’s take n”. “This seat’s
taken” was the most retweeted message of the convention ac-
cording to Twitter. A rough estimate of the reach of that tweet
was 68 million (Boynton, 2012d). Nielsen reported that the
audience for the Romney speech that evening was 30.3 million
(Fouhy, 2012).
The day of the US presidential election 31 million messages
were posted to Twitter, and among them were 455,000 ret-
weeting the Obama Twitter announcement of re-election; “Four
more years” with a photo of the president and his wife hugging.
(Sharp, 2012) The sample we collected the evening of the sixth
and the seventh contained 60,000 retweets of the “Four more
years” tweet. The average number of followers for the 60,000
was 602. The 61,120 retweets were available to 36,833,718
Twitter users. By the end of a week, the total number of ret-
weets of “Four more years” was 815,000. It seems plausible to
assume that the last 350,000 had approximately as many fol-
lowers are the original 455,000. If that is the case then “Four
more years” reached 490,630,000 Twitter users. Of course, that
is a number greater that the population of the United States.
However, on the sixth and seventh of November almost 50% of
the messages posted to Twitter mentioning Obama were from
outside the US (Boynton, 2012a). This message was being ret-
weeted around the world.
The viewership of tweets by ThinkProgress was twice that of
O'Reilly and Jon Stewart. “This seat’s taken” was seen by twice
the number of people who watched the Romney speech. And
“Four more years” reaches half a billion. These numbers chal-
lenge our tacit assumptions about mass media and Twitter. Yes,
Twitter is 400 million messages a day, we say, but it must still
take a “back seat” to the mass medium known as television.
That is the general assumption.
I could pile up more very big numbers, but in this report we
will look at two challenges to the big numbers. Challenges
claim that there is something badly misleading about the big
Estimates of Twitter Use
Pew provides an estimate of Twitter use in the US based on
their surveys. The most recent report was for users as of Febru-
ary of 2012 (Smith & Brenner, 2012). They estimate that 15%
of online adults use Twitter, and that 80% of adults are internet
users. To compare this with the numbers above the percentages
have to be converted into numbers.
US population 18 and older = 308,155,000
80% on line = 246,524,000
15% of online use Twitter = 36,978,600
37 million is fewer than 47 million, and considerably fewer
than 68 million or half a billion, which raises the question about
the counts of Twitter reach.
However, there is another way to estimate Twitter use in the
US Semiocast, a French social media analysis company, esti-
mates that as of July 1, 2012 there were more than 140 million
Twitter accounts in the US and it had grown from 108 million
at the beginning of the year to 142 million by the end of June
(Semiocast, 2012). They used a combination of checking pro-
files and a sample of 1 billion tweets in June to make their es-
The estimates are wildly different. There are three things to
say about the difference. First, they are counting different users.
Pew estimates number of persons with a Twitter account. Sem-
iocast estimates number of accounts. There are many accounts
that are institutional in addition to accounts of individuals. And
institutional accounts are important in political communication
via Twitter. The New York Times has 6.1 million followers and
90,046 tweets as of September 18, 2012, for example. Their
tweets are not all about politics, but many are. And The New
York Times is only one. There are an uncounted number of
institutional accounts that are doing political communication
via Twitter.
Second, they are also wildly different in their estimates of
growth of Twitter use. Pew estimates that the percentage of
online users with Twitter accounts grew from 13% in 2011 to
15% in 2012. That seems very short of the explosive growth
found by almost all other counts. Semiocast, for example, finds
a much greater growth in the first half of 2012—142 million is
1.32 times 108 million. The Pew growth rate seems well below
what others have found.
Third, both methods used for making estimates have flaws.
Semiocast based their estimate on examining 500 million pro-
files on Twitter. But most profiles do not include location. With
the 1 billion samples of Twitter messages they can get time
zones. They can check on language. But they are constructing
an estimate from shards of evidence. It is not a count. Pew sur-
veys have something of the same problem. Pew has now an-
nounced that the response rate for their surveys is 9% (Pew
Research Center, 2012). That does not mean we should ignore
their results. It does mean that, just as with the Semiocast esti-
mates, one would also want other evidence to supplement the
Pew survey results.
The assumption made above is that all of the communication
is from within US boundaries. But that flies in the face of what
we know about Twitter as a technology for global communica-
tion. To show the impact of communication from beyond US
borders these are numbers for the first ten days in November as
we in the US and people around the world reacted to the presi-
dential election.
Table 1 lists the number of users posting one or more mes-
sages mentioning President Obama between November 1 and
November 10. The total sample includes both users from US
time zones and users from not US time zones. The procedure
for collecting and the use of the time zones is found in “It’s a
global shoutout Mr. President” (Boynton, 2012a). While there
is variation in the percentage of users posting from not US time
zone locations the average for the ten days was 42%. This table
is more relevant to the numbers retweeting “Four more years”
than the other two. But it would be a mistake to think that none
of the communication retweeting ThinkProgress or mentioning
“This seat’s taken” were exclusively from the US. We need to
rethink, and re-count, Twitter communication about politics. It
is global beyond our normal expectations.
Where does that leave us? I suggest a safe position is to as-
sume that the number of Twitter accounts being used for politi-
cal communication is somewhere between the Pew estimate and
the Semiocast estimate. That leaves room for some very big
numbers; including a need to recognize how global the com-
munication may have become.
Fake Accounts
We all know there are many fake accounts on Twitter. We do
know that. We just do not know how many nor how many
shown up in communication about politics. So, the existence of
an unknown number of fake accounts is a challenge to any
count of Twitter messages about politics. How many are fake?
How many are not?
In July, 2012 Status People, a small British firm, made avail-
able a tool that estimates the number of fake accounts on the
basis of the activity associated with the account (Status People,
Table 1.
Percent of Twitter messages posted from t ime zones outside the United States.
Nov. 1 Nov. 2 Nov. 3 Nov. 4 Nov. 5 Nov. 6 Nov. 7 Nov. 8 Nov. 9 Nov. 10
Total Sample 216,247 230,888 194,856 270,815 376,799 673,865 659,178 361,486 269,651 294,453
Percent no t US 41.7 35 39.7 39.6 28.1 45.4 53.5 58 46.3 37.7
Copyright © 2013 SciRes.
2012). They draw a sample of 1000 accounts from the most
recent 100,000 accounts that have added themselves as follow-
ers. “On a very basic level spam accounts tend to have few or
no followers and few or no tweets. But in contrast they tend to
follow a lot of other accounts.” This, plus some other adjust-
ments, is their specification of fake accounts. They do not spec-
ify how they determine inactive accounts, but they would be
accounts that do not fall into the definition of fake but have
little activity. Good accounts follow, have followers, and post
messages. Most of the published reports on the use of this tool
have looked at stars: Lady Gaga, Obama and the rest. My fa-
vorite is from the Irregular Times that checked on the US
presidential candidates. (Cook, 2012) He found that the presi-
dential candidate with the fewest fake accounts was Jill Stein,
the Green Party candidate, who had 2% fake accounts, 22%
inactive, and 76% good. And 28% of the followers of President
Obama are fake. Even in this fashion he was “ahead” of his
challenger since only 16% of Romney's followers were fake. Of
course the president had 20 million followers as of October 2,
2012, and growing every day, compared with 1 million for
To estimate the extent of fake followers in political commu-
nication using Twitter two different data collections are em-
ployed. One collection involved a high profile political event.
Twitter messages during the evening of the Romney acceptance
speech were generously gathered for me by Mike Jensen. The
entire political media was focused on this event. It is very dif-
ferent from the other collection. The other analysis is based on
eight searches with a sampling of messages every day for two
months. The eight searches cover a diverse array of subjects,
but none is like the high profile event of the evening of the
Republican National Convention.
Fake Accounts at the Republican Convention
The evening Romney gave his acceptance speech to the Re-
publican Convention Mike Jensen collected a sample of Twitter
messages that mentioned Romney, in one or more forms, and
Obama in one or more forms. He accessed the Twitter stream-
ing API and collected a sample of 591,462 tweets. This is cam-
paign communication at a specific point in the campaign. It is
not simply communication about the Republican Convention,
which is evident since 361,507 messages posted to Twitter
mentioned Romney and 309,395 mentioned Obama. A subset
mentioned both since the total exceeds the number in the sam-
ple. Along with other information the number of followers of
the person posting each tweet was collected.
There is a tremendous range in number of followers. The
fewest are zero since a person without followers can still post a
message. At the top is President Obama with 19,068,078, as of
that evening. There are two ways to get a sense of the range.
One is to look at the people at the very top.
Two features within Table 2 are noteworthy. One, these are
user accounts with a great many followers—millions all. Two,
with the exception of Obama, Perez Hilton, and Eva
Longoria they are accounts from the news media. Peter Cash-
more and Anderson Cooper are the two news persons on the list,
and the rest are news organizations that have Twitter accounts.
These are at the top of a tremendously skewed distribution.
Neither a mean nor a figure is much help in grasping the distri-
bution. One way to get a better feel for the distribution is to use
quintiles. The twitter messages were sorted from most follow-
ers to least followers and then divided into quintiles. The mean
number of followers for each quintile is presented in Table 3.
Among the 120,000 with the fewest followers 3168 had zero
followers. The average number of followers for that fifth of the
Twitter messages was 32. There is a gradual increase in the
mean from 32 to 80 to 300 to 733. Then there is a huge jump to
a mean of 16,227. Since the quintile with the highest mean
contains all of the accounts with multiple million followers it is
clear that the distribution in the top quintile is itself severely
What about fake followers? We examined fake followers in
two sub-distributions. The top 200 Twitter messages were ex-
amined for fake followers. And 100 user accounts with ap-
proximately 1000 followers each were examined. You give the
information to the People Status software one account at a time.
That makes a complete inventory of 590 thousand accounts
The top 200 tweets were chosen because these are the mes-
sages that are going out extremely widely. If you sum all fol-
lowers for all Twitter messages and compare this with the sum
for the top 200 the top 200 messages have 32% of the total
followers for the entire sample. What happens with these Twit-
ter posts makes a very big difference in the flow of messages on
this occasion. The 200 messages were posted by 56 user ac-
counts, which is an average of just under four posts for each
during the evening. Forty of the accounts are related to the
news industry either as an institutional account of as an account
of a person known by working in the news industry.
What could one expect for the Twitter messages produced by
these high visibility accounts? First, these are the kind of ac-
counts bots attach followers to. Being associated with people
who are well known seems a road to respectability in a world
where there is otherwise very little information. So, the expec-
tation is that there would be a high level of fake followers for
these accounts. Second, this is the best of the news industry.
Many Twitter users are likely to follow these sources in much
they same way they “follow” them on TV. With TV they watch
Table 2.
Follower count for top twitter accounts.
Follower Accounts Number of Followers
Obama-barackobama 19,068,078
CNN Breaking News-cnnbrk 8,619,387
New York Times-nytimes 6,003,374
CNN-cnn 5,778,905
Perez Hilton- PerezHilto n 5,501,293
Breaking News-breakingnews 4,555,200 4,222,143
Eva Longor ia-evalongoria 4,158,080 3,831,731
Peter Cash more-mashable 2,987,684
Anderson Cooper-andersoncooper 2,962,324
Total 67,688,199
Copyright © 2013 SciRes. 93
Table 3.
Romney night mean followers by quintile.
0 to 120,000 120,001 to 240,000 240,001 to 360,000 360,001 to 480,000 480,001 to 591,464
32 80 300 733 16,227
Table 4.
Top 200 Twitter messages Romney night.
but do not respond. Twitter reading and not responding should
be a big element of their large following. So, one would expect
inactive followers to be as high or higher than fake followers.
In Table 4, twenty percent of the followers of the se messages
are fake followers. The percentage of inactive followers is 44%.
In writing about the followers of “stars” authors have fre-
quently come close to characterizing the inactive followers as
also not real; accounts that were opened and then the persons
never returned. But that seems a somewhat less likely interpre-
tation in this case. We know that many people use Twitter as a
source of very fast news (Pew Research Center for the People
& the Press, 2012). Since these 200 messages are largely from
sources of the news media it does not seem unexpected that
44% of the followers are inactive. This interpretation is also
consistent with the report of the CEO of Twitter. Dick Costolo
announced that 40% of the people with Twitter accounts do not
tweet, but use Twitter to follow others (Long, 2012). And 36%
of the followers are good; they follow, are followed and post
messages to Twitter. The raw numbers a re take n from a sample.
Comparing the size of this sample with the report about number
of messages from Twitter this looks like roughly a one-third
sample (Boynton, 2012c). In this sample the 500 million that
are inactive or good is a very big communication stream. It is
not the number of people receiving a message, but it is a meas-
ure of the flow of communication that evening. Messages were
going out at a very high volume to followers of the high profile
user accounts.
User accounts with 1000 followers are not Twitter stars.
Unlike the previous analysis these are user accounts, and the
numbers refer to accounts. While they are not stars they are
much more active on Twit te r than usual. In terms of numbe rs of
followers they are at the top of the fourth quintile. So they have
more followers than about 70% of Twitter messages.
What should one expect for these accounts? They are not
famous enough to attract bots and other devices for assigning
them fake followers. So the number of fake followers should be
low. If we understand the Twitter experience of these accounts
as primarily news reporting then we should expect a large
number of inactive followers as with the stars. If we think of
their Twitter experience as very active participation in commu-
nication with both reading and writing then the expectation
would be for fewer inactive followers and more active followers.
Fake Inactive Good
Top 200 20.75% 43.93% 36.32%
Number of messages 130,253,663 275,761,129 227,990,990
Table 5.
Accounts with 1000 followers Twitter messages.
Fake Inactive Good
Accounts 1000 5.84% 13.15% 82.66%
What does this analysis suggest about fake followers in po-
litical communication? This was a special event and they do not
happen very often in politics. The next events like this in the
presidential campaign were the debates between Obama and
Romney. This kind of spike occurred with all of the debates in
the Republican nomination campaign (Boynton, 2012f). Many
more people are following and commenting on politics when
such an event occurs. And the “leading figures” are out. The
number of fake followers is certainly high for the stars, but one
could imagine it higher than 21%. Losing one-fifth reduces the
estimate of the reach of these messages, but that is only for the
leading figures. Once down to the activist level the reduction of
reach has fallen to five percent. In this case it is a 21% loss for
one third of the messages and a good deal smaller loss for the
other two-thirds.
Ongoing Streams of Communication
The next analysis is based on ongoing streams collected by
TweetTronics during July and August of 2012. They take a
small sample of messages every day, but over two months the
collections become rather large. The search terms for the eight
streams are: barackobama, Romney, Palin, #P2, #Teaparty, RT
@ThinkProgress, RT @nytimes, RT @MoveOn. The choice of
streams was intended to produce a diverse set. Three are politi-
cians: barackobama, which is the official user name on the
Twitter account, Romney, and Palin who still has a substantial
following using Twitter. #P2 is the progressive hashtag and
#Teaparty is a hashtag for that movement. These are two po-
litical “dispositions”, which are different streams than the ones
about the politicians. Finally, there are the Twitter messages
retweeting three different “political media” organizations:
ThinkProgress “runs” on social media for liberal causes. Move-
On has been a very successful liberal organization effectively
using email. And then the New York Times. If general state-
ments are appropriate across this diverse set of collections that
would be impressive evidence for the general statements.
In Table 5 the percentage of fake followers drops from 21%
to 6%. The number of inactive messages drops from 44% to
13%. And the number of good accounts increases from 36% to
83%. These are very big changes as you move from the star
accounts to people of middle range activity. The have many
fewer fake followers and far more good accounts following
them. Listening and having something to say is the way to at-
tract followers who also want to listen and have something to
say. The user names suggest that these are primarily individuals
rather than institutions. However, the task is building a basis for
estimating the incidence of fake accounts in political commu-
nication using Twitter so investigating these accounts further
would side track the primary task. The analyses of the 200 user accounts with the most follow-
ers for each of the eight streams were done by students who
Copyright © 2013 SciRes.
share authorship. I added the counts for the 100 user accounts
that had approximately 1000 followers.
The collections are samples so the raw numbers are very par-
tial totals. However comparison between the collections does
give an indicat i on of their relative si ze (see Table 6).
The search for Twitter messages containing barackobama
found the largest number of individual user accounts, at
149,889, with retweeting nytimes messages, search for Romney
and for Palin close behind. Many fewer user accounts were
involved in the messages that retweeted ThinkProgress mes-
sages, searches for #p2 and #teaparty, and even farther down
was retweets of messages of MoveOn with only 8585 user ac-
counts. The total followers are the sum of followers of each of
the user accounts that posted a message to Twitter mentioning
the search term. While there are substantial differences in the
number of user accounts there is much less variation in the
average number of followers per user account. The user ac-
counts found in the search for Romney had substantially more
than any of the others with 2604 followers per account. Then
there were two at 1600s, barackobama and palin, the rest were
in the 1300 except for the user accounts that retweeted Think-
Progress messages with an average of 966 followers.
Not only is there variation in the focus of the streams of
communication, there is also considerable variation in the size
of the streams. It is variation across two important dimensions
for characterizing streams of communication.
The number of followers per user account is highly skewed
in each of the independent data collections.
Table 7 gives the percentage of all accounts that follow the
top 200 for each data collection. The extremes in the table are
readily explicable. Obama and Romney streams are most likely
to include messages from the major news organizations such as
CNN, CBSNews, BBCWorld, and others with large followings.
They are also more likely to be mentioned by celebrities such
PerezHilton, EvaLongoria and DanielTosh who also have very
large followings. #p2, #teaparty, and ThinkProgress are fringe
elements of US politics. Hence they are less likely to have posts
from Obama or major news organizations or major celebrities.
But even for the “fringe” streams the top 200 still get 38% of
all followers of posts in those streams.
The challenge is interpreting the very large number of re-
cipients of Twitter messages about politics from these streams.
To what extent are those numbers produced by large number of
fake accounts? Table 8 shows the number of fake accounts for
each of the streams of messages.
The three that are over 20% are RT @nytimes, with 28%
fakes, barackobama, with 26% fakes, and Romney, with 23%
fakes. These are the most prominent subjects of the eight. They
are the ones most likely to have been mentioned by user ac-
counts that attract bots that set up fake accounts. It is easy to
illustrate how this happens. Table 9 is the number of followers
and the number of fake accounts for the 10 user accounts with
the most followers that mentioned barackobama in a tweet.
Obama is clearly at the top with 19 million followers, and that
was to increase substantially during the election campaign.
Like Obama the rest are stars. CNN, CNNEE, and mashable
are media “stars”. And the rest are entertainment stars including
Carmelo Anthony an NBA entertainment star. Stars attract fake
Table 6.
Comparing the eight d ata collection.
Total User Accounts Total Followers Average Followers
barackobama 149,889 249,924,216 1667
RT @nytimes 134,339 186,646,099 1389
Romney 122,081 318,012,582 2604
Palin 119,236 201,333,569 1691
RT @thinkpro gr ess 51,615 51,445,535 996
#p2 39,948 52,668,719 1318
#teaparty 28,249 39,232,240 1389
RT @moveon 8585 11,928,535 1389
Table 7.
Percentage all accounts that follow top 200.
barackobama romney RT @moveon palin RT @nytime s #p2 #teaparty RT @thinkprogress
53% 52% 51% 47% 40% 38% 38% 38%
Table 8.
Fake accounts.
barackobama Romney RT @moveon Palin RT @nytimes #p2 #teaparty RT @ thinkprogres s
Top 200 26% 23% 5% 19% 28% 16% 16% 18%
1000 2% 3% 2% 2% 2% 1% 1% 2%
Copyright © 2013 SciRes. 95
Table 9.
Top followers barackobama.
User Account Followers Fake Followers
Obama 19,027,066 36%
danieltosh 6,216,847 18%
CNN 5,657,090 39%
PerezHilton 5,358,086 22%
EvaLongoria 4,101,633 29%
BBCBreaking 3,855,283 27%
SarahKSilverman 3,126,763 14%
mashable 2,981,526 9%
CNNEE 2,972,160 27%
carmeloanthony 2,450,160 15%
obama2012 190,529 7%
Table 10.
Good accounts.
barackobama Romney Palin R T @nytimes #p2 #teapa rty RT @thinkprogress
Top 200 44% 38% 44% 40% 59% 66% 50%
1000 87% 83% 86% 85% 93% 94% 90%
accounts. For the top 200 the pattern is straightforward. The “stars”—
barackobama, Romney, Palin and RT @nytimes—are men-
tioned in Twitter messages that go to only 40% good user ac-
counts. That is balanced by roughly 25% of user accounts re-
ceiving messages mentioning them that are fake accounts. That
leaves roughly 30% as inactive accounts, which is in the
neighborhood of what Costolo tells us we should expect. The
“fringe”—#p2, #teaparty, and RT @thinkprogress—are men-
tioned in messages that go to followers who are considerably
more likely to be good accounts. The messages mentioning
#teaparty go to a high of 66% good accounts.
The other way to illustrate this is by comparing the number
of followers and the percentage of followers that are fake for
Obama and Obama2012. Twenty million followers for barack-
obama are accompanied by 36% fake followers. One hundred
and ninety thousand for obama2012 only attracts 7% fake ac-
The streams mentioning the less famous—Palin, #p2, #tea-
party and RT @thinkprogress—are included in Twitter mes-
sages with fewer fake followers. For them it is between 16%
and 19%. So fake followers are still a big part of the reach of
messages mentioning them. But it is a smaller part than for the
stars. RT @moveon is the interesting deviant case. One might
suggest that this is what little social media fame gets you. They
have, by far, the fewest user accounts retweeting their messages,
and those accounts have, by far, the fewest number of fake
The user accounts with approximately 1000 followers have
almost all good followers. The accounts mentioning the stars
have followers who are good accounts in the 80% range. The
fringe has followers who are 90% good. They have almost no
fake followers. They are overwhelmingly connected with other
good user accounts.
The contrast between the top 200 accounts and the accounts
that have approximately 1000 followers is striking. These ac-
counts, drawn from the same streams, have one or two percent
fakes. Only the accounts from the Romney stream have as
many as thre e percent fake s. This is very similar to th e findings
from the Romney speech. By the time you get to accounts with
a thousand followers you are in a different world from the stars.
They have no attraction for fakes.
The reason for examining the validity of the big numbers is
that they portend a dramatic reorganization of the public do-
main. We are moving from a broadcast-audience public domain
to a much more elaborate co-motion in which broadcast media
and social media interact in very new ways (Boynton, 2012b).
If the numbers are as big as they appear to be, then “public” is
going to become something new. One writer quipped that
post-debate TV anchors are now basic for telling people what
was said on Twitter during the debate. But people are not going
to need this service much longer because they are engaged in
the communication during the debate. They will not need
What about good accounts; the accounts that follow, are fol-
lowed and post messages? Table 10 gives the percentage of
followers receiving messages mentioning each of the eight
subjects that are good accounts. It gives the percentages for the
top 200 accounts mentioning each of the eight and for 100 for
each subject with approximately 1000 followers.
Copyright © 2013 SciRes.
someone to tell them what they have seen first hand.
So, what should be the conclusion?
What about the number of accounts? There are two estimates:
Pew estimates 37 million and Semiocast estimates 170 million.
Both are rough approximations. Since it is easy to find follow-
ers of messages that add up to numbers in between, the two in
between seems a safe estimate.
What about fake accounts? For high profile communication it
seems reasonable to estimate that roughly 50% of the total
numbers of followers are following the top 200 users who post
to Twitter, and half are following people with less stardom. The
top 200 seem to have about 25% fake followers and 30% inac-
tive followers. But the rest seem to have very different numbers.
They are followed by very few fake accounts, and their follow-
ers are overwhelmingly good, or active, user accounts. If you
count only the fake followers of the top 200 then the total
would be reduced by 12.5%. That could be increased to 15% or
slightly more due to the fake followers of the not top 200. It
looks like no more than 20% fake followers in those large
numbers. So you could reduce the numbe r who saw “This seat’s
taken” from 68 million to 55 million. That is still substantially
more than saw the speech on TV. RT @thinkprogress is men-
tioned in messages by user accounts that have 18% fake fol-
lowers. That would suggest 3.9 million receiving retweets of
ThinkProgress tweets instead of 4.7 million. But that is still
greater than the leading lights on TV.
The principal conclusion is that fake accounts are found in
communication about politics. They exaggerate the number of
recipients of Twitter messages because fake accounts do not
read. Only the not-fakes read. However, it appears that the most
one would expect is about 20%. That is a substantial number,
but it is not so large that the large number of recipients of
Twitter messages now looks small. The public domain is be-
coming something new.
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