TELKOM
NIKA
, Vol.14, No
.2, June 20
16
, pp. 757~7
6
1
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v14i1.3041
757
Re
cei
v
ed
No
vem
ber 1
5
, 2015; Re
vi
sed
March 17, 20
16; Accepted
April 6, 2016
Critical Success Facto
r
in Monetizing Blog
Andika
Rizk
y
,
Bens Pardamean*
Information S
ystem Manag
e
m
ent Graduat
e
Program, Bi
na
Nusantar
a Uni
v
ersit
y
, Jak
a
rta
,
Indonesi
a
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: bpard
a
me
an
@bin
us.ed
u
A
b
st
r
a
ct
Blog
usa
g
e
h
a
s
transfor
m
e
d
from
its i
n
itia
l
function
as
a
digit
a
l
diary
i
n
to a
le
giti
mate
form
of
ma
instre
a
m
medi
a. Now
aday
s any
me
mber
of the public
can ge
ner
at
e i
n
co
me fro
m
bl
ogg
ing thr
oug
h
mo
neti
z
at
ion
e
v
en tho
u
g
h
va
rious factors af
fect the resu
lts
of this proces
s. F
our
factors w
e
re identifi
e
d:
traffic,
search eng
ine opti
m
i
z
ation
(SEO), p
o
st freque
nci
e
s, and
me
dia
usag
e. T
h
is study ex
a
m
in
es
the
level
of i
m
por
tance
of thes
e fact
ors thr
o
ugh
practic
a
l
imple
m
entati
o
n
into
blo
g
s th
en
qua
ntitativ
ely
deter
mi
ne w
h
ic
h factor
is critic
al for
the s
u
cce
ss of b
l
og
mo
n
e
ti
z
a
tio
n
. An
e
m
p
i
rica
l a
n
a
l
ys
is b
a
sed
o
n
thir
ty
sampl
e
s of
bl
o
g
s w
e
re
perfor
m
e
d
to
ass
e
ss
the
i
m
pact
of
the rec
e
ive
d
in
come. T
h
e
fin
d
i
ng
show
e
d
th
at
the reven
ue for
most of the bl
ogs in
cr
ease
d
after the imple
m
e
n
tatio
n
of critical factors w
i
th SEO being t
h
e
m
o
st critical of all the factors.
Ke
y
w
ords
: Blo
g
, Moneti
z
e
,
Cr
itical Succ
ess F
a
ctors, Search Engi
ne Opti
mi
z
a
t
i
o
n
Copy
right
©
2016 Un
ive
r
sita
s Ah
mad
Dah
l
an
. All rig
h
t
s r
ese
rved
.
1. Introduc
tion
The Internet
tec
h
nologic
a
l development has le
d to
a cultural
s
h
ift, es
pec
i
ally
with the
routine
s
by which
the
gen
e
r
al p
ubli
c
spe
nd its le
i
s
ure
time. Surfing
the world
wi
d
e
web
thro
u
gh
the Internet i
s
an a
c
tivity
that is cu
rre
n
t
ly
more wid
e
sp
rea
d
than
watchi
ng tel
e
vision [1]. The
con
s
e
que
nce
s
fro
m
in
crea
sed
u
s
ag
e of
Internet
al
so
mean
s that th
ere i
s
an i
n
crease in fu
ndi
ng
for Intern
et a
d
versti
sing,
which m
o
tivates the n
eed to
determi
ne o
pportu
nities t
hat wo
uld lea
d
to
improvem
ent
s in th
e effectiveness of o
n
line a
d
ve
rtisement [2]. Blog is one
av
enue to
adve
r
tise
online.
Blog is a
we
bsite d
epe
nd
ent upo
n re
g
u
lar u
pdate
of its co
ntent
s. Since
the
n
, it has
evolved from
a medi
a form cate
ring
to
a small n
u
m
ber
of niche
grou
ps ba
se
d
sha
r
e
d
inte
rests
[3] into a seri
es of po
sts th
at is more like a dia
r
y with
a reverse
ch
ronol
ogi
cal o
r
der. Blog
s ha
ve
achi
eved m
a
i
n
stre
am
statu
s
in th
e me
di
a sin
c
e
it
is
eas
y
to obtain.
For
s
o
me, it
has
bec
o
me a
daily necessit
y
, similar to a daily perio
dical o
r
ne
wspape
r [4]. This is e
s
pe
ciall
y
true for blo
g
s
that often or
prima
r
ily po
st on topi
cs
no
t covere
d by
the main m
e
dia. Addition
ally, informati
on
from blog
s sp
read
widely a
nd qui
ckly, providing dee
p
e
r and u
pdat
ed cove
rag
e
[4]. Blog is often
use
d
for po
sting opinio
n
s a
nd expre
s
sio
n
s by its
write
r
s, expan
ding
its
content type com
pared
to
that of the m
edia [5]. Sha
r
ed inte
re
st be
tw
een blog re
aders and
th
e
blog writers
can
create bl
og
ring
s. Th
e j
o
i
n
ing
and
lin
king of
severa
l blog
ri
ng
s t
h
rou
g
h
com
m
enting
or
subscri
b
ing
th
en
cre
a
te
a bl
og
osp
here [6].
Blog al
so
ha
s sp
eci
a
l feat
u
r
es that
are
n
o
t as p
r
omin
e
n
t in oth
e
r forms
of media, su
ch as man
age
ment system
of cont
ent
s, ease of u
s
e by any
member of the general
publi
c
, a
r
chive-o
r
iente
d
structure, info
rm
ation m
ana
ge
ment b
a
sed
o
n
the
late
st in
formation
po
st,
and fo
rmatio
n of a
blo
g
co
mmunity
throug
h o
n
lin
e inte
ra
ction
[7]. Blog i
s
differe
nt th
en
microblo
g
. Micro
b
log
s
su
ch as fa
ceb
o
o
k an
d twitte
r use many
ways to
rele
ase info
rmati
on
namely u
s
in
g web
pag
e, mobile p
hon
e, comm
uni
cation, softwa
r
e an
d emai
l [8]. Moreo
v
er
microblo
g
s d
e
rive othe
r ap
plicatio
ns, i.e., microbl
og m
a
rketing [9].
The pe
rspe
ctive of succe
s
s in the previo
us
stu
d
y on b
l
og su
cce
ss f
a
ctors
cond
u
c
ted by
Du an
d Wag
ner [10] is from a tech
nol
ogica
l stan
d
point. The st
udy analyzed
the impact
of
techn
o
logy u
s
ed i
n
126
blog
s from t
he top 1
00
l
i
sting of the
Tech
no
rati
web
s
ite, an
d
the
su
ccess
wa
s mea
s
u
r
ed
b
y
the num
be
r of in
bou
nd
links to a
we
blog. Safran
and Ka
ppe
[11]
examine blo
g
succe
s
s fact
ors
by analysing activities,
post freq
uen
cie
s
, the num
ber of ima
g
e
s
,
comm
ents
given, comm
en
ts re
ceived,
gue
stboo
ks
receive
d
, and
guestb
oo
ks
given. The
study
indicates that
getting involved in the
com
m
unity is
the
cru
c
ial
su
cce
ss fa
ctor fo
r b
l
oggin
g
. Coh
e
n
and K
r
ishna
murthy [12]
analyze bl
og
co
mmunity
by co
unting
the hype
rlin
k and
conn
ection
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 2, June 20
16 : 757 – 76
1
758
betwe
en type
or topi
c a
s
a ch
an
ce for
cre
a
ting
re
lati
ons i
n
blo
g
community. Seba
stian, et
al.,
[13] examine
whi
c
h
of the
moneti
z
ing
tech
niqu
es
is
the mo
st po
p
u
lar. Adve
rtising i
s
the
mo
st
comm
only used method b
e
ca
use of its variety in
form and tech
ni
que. A study on fashio
n a
n
d
lifestyle
blo
g
monetization
has
be
en
co
ndu
cted
by a
nalyzin
g th
re
e blo
g
s.
It in
dicate
d that
each
of thre
e bl
og
s ha
s it
s o
w
n
method
to m
o
netize,
su
ch
as i
nbo
und
m
a
rketi
ng, as well as sale
s and
affiliated marketing [14].
Blog ha
s ma
ny function
s
and pu
rp
ose
s
, one of whi
c
h is serving
a
s
an alte
rnat
e sou
r
ce
of income th
rough
mon
e
tization. The
r
e
are va
ri
o
u
s monetization method
s su
ch
a
s
a
d
vertisi
ng,
paid
contents, affiliated m
a
rketi
ng, donation,
pai
d subscri
p
tion,
and
consulting [13]. However,
variou
s facto
r
s influe
nce th
e level of rev
enue. Fo
ur
o
f
these fa
ctors a
r
e traffic,
sea
r
ch en
gin
e
optimizatio
n (SEO), po
st freque
nci
e
s, a
nd medi
a
u
s
age. In this
study, we inte
nd to dete
r
mi
ne
whi
c
h of these factors is th
e critical fact
o
r
for a su
cce
s
sful blog mo
n
e
tization effort.
2. Rese
arch
Metho
d
The sample
of blogs
used
for analy
s
e
s
were sele
cte
d
throu
gh ind
one
siao
nline.
net, the
site of
a
com
pany e
nga
ge
d in
bloggi
ng
and
its
m
o
n
e
tization. T
h
irty blogs u
s
in
g Word
press
for
their
Co
ntent
Man
agem
en
t System
were cho
s
en
du
e to th
eir
sta
t
us of
bei
ng l
o
we
st
reven
u
e
-
gene
rating bl
ogs a
s
of M
a
y 2015. For thirty days,
the su
ccess
factors were
impleme
n
ted
in
these bl
og
s to observe ch
ange in reven
ue.
The data
were colle
cted in
a timely-based order
with
daily colle
ction for ea
ch b
l
og fro
m
its spo
n
sore
drevie
ws.
c
om
account, wh
ich is o
w
n
e
d
by indone
si
aonlin
e.net. The traffic d
a
ta
gathered eve
r
y wee
k
o
n
Monday from
alexa.com
a
nd chkm
e.co
m wa
s min
e
d
for the stu
d
y as
well. The
SEO data
we
re
colle
cted
every we
ek
on Mon
day
from chkme.
com. Th
e p
o
st
freque
nci
e
s
data were
co
llected f
r
om t
he blo
g
itse
lf
by insp
ectin
g
the ent
ry list po
sted ev
ery
wee
k
on M
o
nday. The
m
edia u
s
a
ge
d
a
ta were
coll
ected
from
th
e blog
itself
by co
unting t
h
e
media ne
w a
r
ticle every we
ek on Mo
nda
y.
In this
re
sea
r
ch,
the p
r
ofi
l
e blo
g
data
were
al
so
co
llected. T
he
profile
blog
d
a
ta are
PageRan
k, P
age Auth
ority
,
Domai
n
Aut
hority, and
ni
che.
Pa
ge
Ra
nk (PR)
d
a
ta wa
s colle
cted
at
the end
of t
he expe
rime
nt throu
gh
chkme.
com.
F
o
r the
Page
Authority (P
A) and
Dom
a
in
Authority (DA) of ea
ch
blog, the d
a
ta we
re
colle
cted at the
end of the
e
x
perime
n
t usin
g
che
c
kmo
z
.co
m
. For niche, the data wa
s
provide
d
by spon
sored
r
eview.com and
also
colle
cted
at
the end of the
experime
n
t.
After coll
ectin
g
blog
p
r
ofile
s, setting rele
vant indep
en
dent a
nd d
e
p
ende
nt varia
b
l
e data
wa
s d
one.
T
he d
a
ta th
en
we
re
an
alyzed u
s
in
g p
a
ired t-te
st, correlation,
re
gression,
and
t
w
o-
way ANOVA.
Paired t-te
st
wa
s used fo
r dete
r
minin
g
the reven
u
e
differen
c
e b
e
fore a
nd aft
e
r
su
ccess facto
r
impleme
n
tation.
3. Results a
nd Analy
s
is
The reve
nue
wa
s cla
s
sifie
d
and an
alyzed by the we
ek, setting th
e before and
after time
frame. Ta
ble
1 su
mma
rizes the
re
sult
of the pai
re
d t-test a
naly
s
is fo
r p
r
e
-
a
nd po
st-reve
nue
comp
ari
s
o
n
p
e
r
wee
k
. Ta
bl
e 1 indi
cate
s
that
the average
revenu
e i
n
crea
sed
afte
r imple
m
entin
g
the su
cce
ss f
a
ctors from the sec
ond
week to th
e fourth wee
k
. T
here
are sig
n
i
ficant differe
nce
s
in the
avera
g
e
reve
nue
be
fore a
nd
after the impl
eme
n
tation for th
e entire m
ont
h with
a
p-val
ue
of 0.049. The
fourth wee
k
of implement
ation ha
s the lowe
st p-valu
e (0.016
).
Table 1. Co
m
pari
s
on
s of Weekly Pr
e a
n
d
Post Implem
entation Revenue
Ti
me
Pre Post
p-value
Mean
(USD)
SD
(USD)
Mean
(USD)
SD
(USD)
30
da
y
s
0.312
1.627
0.551
3.267
0.049*
First
week
3.1 4.626
2.533
4.812
0.649
Second
w
eek
1.217
2.473
2.567
5.008
0.170
Third
w
eek
2.933
4.646
4.933
10.295
0.243
Fourth
week
0.867
2.255
7.333
13.593
0.016*
*Significant at p < 0.05
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Critical Succe
ss F
a
cto
r
in Monetizi
ng Bl
og (Andi
ka
Ri
zky)
759
For fu
rthe
r a
nalysi
s
, the
revenue
for
p
o
st a
nd
pre
i
m
pleme
n
tatio
n
was an
alyzed u
s
in
g
ANOVA, as
shown in Ta
bl
e 2, yielding
a p-valu
e of
0.001. Fo
r de
eper
analy
s
is, the reven
u
e
for
pre impl
emen
tation wa
s an
alyzed u
s
in
g Post Ho
c Te
st.
Table 2. ANO
VA Revenue
Pre Impleme
n
tation Test
Re
sult
Sum of Squares
df
Mean Squares
F
p-value
Between Groups
284.240
3
94.747
6.329
0.001*
Within Groups
1736.542
116
14.970
Total 2020.781
119
*Significant at p < 0.05
In orde
r to de
termine
whi
c
h we
ek i
s
si
g
n
ific
antly diffe
rent from
oth
e
r wee
ks, the
revenu
e
data we
re an
alyzed
u
s
ing
post ho
c
te
st
.
Table
3
sh
ows that th
ere a
r
e
sig
n
ificant differen
c
es
betwe
en the first an
d se
co
nd we
ek a
s
well a
s
the first and third wee
k
. The p
-
value for We
ek 1
and 2
wa
s 0.
003 a
nd the
p-value f
o
r
week
1 an
d
week
2 was 0.
001. Both of
them have
a
p-
value < 0.05.
Table 3. Post
Hoc T
e
st of Pre Impleme
n
tation
Week
(I)
Week
(J
)
Mean
Difference
(I-J
)
p-value
Revenue per
we
ek pre-implemen
tation
1
2 3,517
0.003*
3 1,800
0.278
4 3,867
0.001*
2
1 -3,517
0.003*
3 -1,717
0.319
4 0.350
0.985
3
1 -1,800
0.278
2 1,717
0.319
4 2,067
0.170
4
1 -3,867
0.001*
2 -0.350
0.985
3 -2,067
0.170
*Significant at p < 0.05
The
se
con
d
significa
nt diffe
ren
c
e
of ANO
VA revenu
e t
e
st
wa
s a
naly
z
ed
ea
ch
we
ek
after
impleme
n
tation. Tabl
e 4
sho
w
s n
o
si
gnifica
nt
diffe
ren
c
e
betwe
en the
group
s. The
ave
r
a
ge
revenu
e for e
a
ch
wee
k
in
creased after i
m
pleme
n
tatio
n
.
Table 4. ANO
VA Post Implementation
Revenue Te
st Re
sult
Sum of Squares
df
Mean Squares
F
p-value
Between Groups
89.267
3
29.756
0.402
0.752
Within Groups
8,588.600
116
74.040
Total
8,677.867
119
*Significant at p < 0.05
The inde
pen
dent variabl
e
with the highest co
rrelati
on to revenu
e wa
s determined th
e
Pearson correlation test. The reven
u
e
was u
s
ed a
s
a depe
nde
nt variable in
this test. The
indep
ende
nt variable
s
u
s
e
d
in this test were Alexa,
Backlin
k, SEO sco
re, p
o
st frequ
en
ci
es,
media
usage
, PageRan
k, Dom
a
in Aut
hority (DA),
Page Autho
r
i
t
y (PA), and
nich
e. Tabl
e 5
indicates th
at there
we
re t
w
o o
u
t of nin
e
inde
pen
de
nt variable
s
with p
-
value
s
< 0.05,
nam
ely
SEO and ba
cklin
k. SEO had a p-value of
0.013 and b
a
ckli
n
k h
ad a p
-
value of 0.04
2.
Simple re
gre
ssi
on te
st wa
s u
s
ed to m
easure th
e impact of in
d
epen
dent variable
s
o
n
revenu
e. The
indepen
dent
variable u
s
e
d
for simple
regre
s
sion te
st were Alexa,
Backlin
k, SEO
score, p
o
st freque
nci
e
s, m
edia
u
s
ag
e, PageRan
k, Domain Autho
r
ity (DA), Page Authority (PA),
and ni
ch
e. Table 6
sum
m
ari
z
e
s
the li
near
re
gre
s
si
on test m
ode
l. Nine in
dep
ende
nt varia
b
le
s
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 2, June 20
16 : 757 – 76
1
760
had 40.6% contributio
n to the revenue
after im
plementation an
d 59.4% is affected by othe
r
variable
s
.
Table 5. Inde
pend
ent Co
rrelation of Vari
able
s
with Re
venue
Variable Pearson
Cor
r
elat
ion
p-value
Alexa
0.005
0.977
PR 0.157
0.409
DA 0.165
0.385
PA 0.282
0.131
Backlin
k 0.373
0.042*
SEO 0.449
0.013*
Post Frequencie
s
0.054
0.776
Media Usage
0.208
0.27
*Significant at p < 0.05
Table 6. Sum
m
ary of Linea
r Reg
r
e
s
sion
Test Mod
e
l
Model
R Square
Adjusted R Squa
re
Std. Error
of the
Estimate
1 .406
.138
9.46529
Furthe
r a
naly
s
is with li
nea
r regressio
n
wa
s d
one to
find ind
epe
ndent va
riabl
es
wit
h
signifi
cant eff
e
ct. As
sho
w
n in Ta
ble 7,
only one
ind
epen
dent va
riable, SEO,
with a
p-valu
e of
0.049, had a
signifi
cant effect on revenu
e afte
r the implementatio
n of succe
s
s factors.
Table 7. Line
ar Re
gressio
n
A
nalysis of
Indepe
nde
nt Variabl
e
Variable Beta
t
p-value
Alexa
-.495
-1.926
.068
PR .087
.461
.649
DA .243
1.283
.214
PA -.183
-.886
.386
Niche -.200
-.979
.339
Backlin
k .336
1.374
.185
SEO .435
2.098
.049*
Post Frequencie
s
-.017
-.064
.949
Media Usage
.174
.597
.557
*Significant at p < 0.05
Two
-
way ANOVA wa
s used to dete
r
mi
ne whi
c
h i
n
d
epen
dent variable
s
had
si
gnifica
nt
impact
s
o
n
re
venue. Th
e in
depe
ndent va
riable
s
we
re
cla
ssifie
d
into
two
gro
u
p
s
a
s
in
the p
a
ire
d
t-test. Alexa, Backli
n
k, S
E
O score, p
o
st
freq
uen
cies, medi
a
usa
ge, Page
Ran
k
, Doma
in
Authority (DA
)
and Pa
ge
Authority (PA) we
re u
s
e
d
as ind
epe
nde
nt variable
s
. The re
sult fro
m
two-way ANOVA test (T
a
b
le 8
)
con
c
lu
ded that fr
om
the eig
h
t ind
epen
dent va
riable
s
that
were
tested, o
n
ly
one i
ndep
en
dent vari
able
had
a
signi
fi
cant im
pa
ct
on revenu
e a
fter implem
e
n
ting
the su
ccess factors, which
wa
s
SEO with a p-value of
0.005.
Table 8. Two-way ANOVA
Analysis
Usi
n
g Indepe
nde
n
t
Variable
Variable
T
y
pe I
II Sum of
Square
df
Square Mean
F
p-value
Alexa
187.237
1
187.237
1.196
.300
Backlin
k 580.167
1
580.167
3.705
.083
PR 107.538
1
107.538
.687
.427
DA 602.079
1
602.079
3.845
.078
PA .000
0
.
.
.
SEO 2036.831
1
2036.831
13.007
.005*
Freque
nc
y .000
0
.
.
.
Media .000
0
.
.
.
*Significa
nt at p < 0.05
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Critical Succe
ss F
a
cto
r
in Monetizi
ng Bl
og (Andi
ka
Ri
zky)
761
4. Conclusio
n
The
eve
r
cha
nging wo
rld o
f
blog creates
many
motiva
tions fo
r blo
g
moneti
z
ation
.
There
are
many fa
ctors that influ
ence the
lev
e
l of
revenu
e
gen
erate
d
.
This stu
d
y was
motivated
by
these
effort
s t
o
an
alyze
fou
r
fa
ctors: traff
i
c,
sea
r
ch e
n
g
ine
optimiza
t
ion (SEO
), p
o
st frequ
en
cies,
and
media
u
s
ag
e. By imp
l
ementing
the
s
e fo
ur facto
r
s into
ou
r
sa
mple of
blo
g
s
, it
was
sho
w
n
that the revenue of most
blogs were
incre
a
s
ed. After analyzi
ng all the data colle
cted
, we
determi
ned t
hat sea
r
ch e
ngine o
p
timization (SEO
) is
the critical factor fo
r a succe
ssful bl
o
g
monetization effort.
Referen
ces
[1]
T
r
uong Y, McColl R, Kitch
en
P. Practitioners
’
Perce
p
tions
o
f
Advertisin
g Strategi
es for Di
gital Me
di
a.
Internatio
na
l Journ
a
l of Adver
t
ising
. 20
10; 29
(5): 709-7
25.
[2]
W
u
S, Lin CS, Lin J. An Empiric
a
l Investig
atio
n o
f
Online Use
r
s'
Ke
yw
or
d
Ads Search
Behav
iours.
Onlin
e Infor
m
ati
on Rev
i
ew
. 2011; 35(2): 1
77-
193.
[3]
Husse
y T
.
Cre
a
te Your O
w
n
Blog. Seco
nd e
d
itio
n. India
nap
olis: Sams Pub
lishi
ng. 20
12.
[4]
Hu N, Do
ng Y
,
Liu L, Ya
o L
J
. Not All T
hat Gli
tters Is Gold T
he Effect of Attention
an
d Blo
g
s on
Investors’ Investing Behaviors.
Journal of A
ccounti
ng,
Aud
i
ting & F
i
na
nce
. 2013; 28(
1): 4-19.
[5]
Den
g
L, Yuen
AH.
T
o
w
a
r
d
s
a F
r
ame
w
ork
for Educatio
n
a
l Affordanc
es
of Blogs.
Co
mp
uters &
educ
atio
n.
201
1; 56(2): 44
1-4
51.
[6]
Cha
u
M,
Xu
J. Busi
ness
Intell
ige
n
ce
in B
l
ogs:
Un
derstan
din
g
C
onsum
er Inter
a
ctions
a
n
d
Communities.
MIS quarterly
. 201
2; 36(4): 11
89-1
216.
[7]
Cho S, H
uh J.
Conte
n
t Ana
l
ysis of Cor
porat
e Blo
g
s as a
R
e
lati
onsh
i
p M
a
nag
ement T
ool
.
Corporate
Co
mmun
icati
o
ns: An Internati
ona
l Journ
a
l
. 2
010; 15(
1): 30-
48.
[8]
Hu Y. C
l
usteri
n
g
-Base
d
H
o
t T
opic
Detecti
ng
in C
h
in
ese
Mic
r
obl
og.
T
E
LKO
M
NIKA Indo
ne
sian J
our
nal
of Electrical En
gin
eeri
n
g
. 20
1
4
; 12(3): 20
96-
210
3.
[9]
Yuan J, W
ang
B, Ding SA Real-tim
e Sea
r
ch
Structure and Cl
assific
a
tion Al
gorithm
of Microblo
g
Based o
n
Parti
a
l Inde
xing.
T
E
LKOMNIKA Indo
nesi
a
Jour
nal of Electric
a
l
Engi
neer
ing
. 201
4;
12(3):
227
4-22
77.
[10]
Du HS, W
agn
er C. W
eblog
Succe
ss: Expl
orin
g the Rol
e
of
T
e
chnol
og
y
.
Int. J
.
Hum
.
-Comput. Stud
.
200
6; 64(9): 78
9-79
8.
[11]
Safran C, K
a
ppe F
.
Succ
e
ss F
a
ctors in
A W
eblo
g
C
o
mmunit
y
.
Jour
nal
of Un
ivers
a
l C
o
mput
er
Scienc
e.
200
8; 14(4): 546-
55
6.
[12]
Coh
en E, Krish
namurth
y B.
A Short W
a
lk in the Blo
g
ista
n.
Comput. Networks.
2006; 50(
5)
: 615-63
0.
[13]
Muller
S, Gos
w
a
r
ni
S, Krcm
ar H.
M
oneti
z
i
ng B
l
ogs:
Rev
enu
e Stre
a
m
s
of Ind
i
vi
dua
l
Blogs.
19th
Europ
e
a
n
Conf
erenc
e on Infor
m
ation S
y
stem
s, ECIS 2011. Helsi
n
ki, F
i
nl
an
d. 2011.
[14]
Rud
o
lp
h F
.
Successful F
a
shi
on an
d Lifest
yl
e Blogs-T
he Busin
e
ss.
Marke
t
ing an
d Ben
e
fi
ts
. 2013.
Evaluation Warning : The document was created with Spire.PDF for Python.