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#Forward!: Twitter as Citizen Journalism in the Wisconsin Labor Protests
Unformatted Document Text:  Twitter
and
the
Wisconsin
Labor
Protests

 
 19
 (#teaparty,
#StateSOS
and
#solidarity)
and
two
were
general‐use
tags
to
denote
 conservative
and
progressive
tweets
(#tcot
and
#p2,
respectively).
 In
order
to
situate
the
community
of
Twitter
users
within
this
dataset,
we
 first
carried
out
an
analysis
of
the
user
number
that
the
message
originated
from
as
 well
as
the
username
associated
with
that
user
number.

A
total
of
89,922
unique
 twitter
users
were
responsible
for
the
775,030
tweets
within
the
data
corpus.
 Certain
user
numbers
were
associated
with
multiple
usernames;
these
were
 collapsed
into
the
most
frequently
used
username
so
that
every
user
number
was
 associated
with
a
unique
username.

An
overwhelming
majority
of
these
users
–
89
 percent
–
sent
10
or
fewer
tweets,
with
about
54.9
percent
of
users
contributing
just
 one
tweet
each.
Approximately
10
percent
of
the
users
were
responsible
for
78.7
 percent
of
the
tweets
that
were
sent
during
our
observation
period.
The
highest
 number
of
tweets
sent
by
one
user
was
5,160
and
37
users
contributed
more
than
 1,000
tweets
each
(see
Figure
1).
 Next,
we
examined
the
timeline
of
the
tweets
and
the
frequency
with
which
 they
were
sent
during
the
observation
period.
This
analysis
was
done
initially
by
 looking
at
the
frequency
of
tweets
on
a
day‐by‐day
basis
and
then
subsequently
by
 hour.
The
day‐by‐day
analysis
(see
Figure
2)
indicates
activity
ranging
from
30,000
 to
50,000
tweets
per
day
in
the
first
two
weeks,
a
steady
decline
in
the
third
week
 tapering
down
to
11,896
tweets
on
day
20,
followed
by
a
sharp
increase
at
the
end
 of
our
observation
period.
 Analysis
by
hour
follows
a
similar
pattern
and
indicates
the
specific
hours
 during
which
traffic
spiked
and
ebbed.
The
top
three
hours
of
tweet
activity
were
10


Authors: Veenstra, Aaron., Iyer, Narayanan., Bansal, Namrata., Hossain, Mohammad., Park, Jiwoo. and Hong, Jiachun.
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background image
Twitter
and
the
Wisconsin
Labor
Protests


19

(#teaparty,
#StateSOS
and
#solidarity)
and
two
were
general‐use
tags
to
denote

conservative
and
progressive
tweets
(#tcot
and
#p2,
respectively).

In
order
to
situate
the
community
of
Twitter
users
within
this
dataset,
we

first
carried
out
an
analysis
of
the
user
number
that
the
message
originated
from
as

well
as
the
username
associated
with
that
user
number.

A
total
of
89,922
unique

twitter
users
were
responsible
for
the
775,030
tweets
within
the
data
corpus.

Certain
user
numbers
were
associated
with
multiple
usernames;
these
were

collapsed
into
the
most
frequently
used
username
so
that
every
user
number
was

associated
with
a
unique
username.

An
overwhelming
majority
of
these
users
–
89

percent
–
sent
10
or
fewer
tweets,
with
about
54.9
percent
of
users
contributing
just

one
tweet
each.
Approximately
10
percent
of
the
users
were
responsible
for
78.7

percent
of
the
tweets
that
were
sent
during
our
observation
period.
The
highest

number
of
tweets
sent
by
one
user
was
5,160
and
37
users
contributed
more
than

1,000
tweets
each
(see
Figure
1).

Next,
we
examined
the
timeline
of
the
tweets
and
the
frequency
with
which

they
were
sent
during
the
observation
period.
This
analysis
was
done
initially
by

looking
at
the
frequency
of
tweets
on
a
day‐by‐day
basis
and
then
subsequently
by

hour.
The
day‐by‐day
analysis
(see
Figure
2)
indicates
activity
ranging
from
30,000

to
50,000
tweets
per
day
in
the
first
two
weeks,
a
steady
decline
in
the
third
week

tapering
down
to
11,896
tweets
on
day
20,
followed
by
a
sharp
increase
at
the
end

of
our
observation
period.

Analysis
by
hour
follows
a
similar
pattern
and
indicates
the
specific
hours

during
which
traffic
spiked
and
ebbed.
The
top
three
hours
of
tweet
activity
were
10



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