34 3. HOW SOCIAL CONTEXT HELPS
˛
i
D
˛
C
X
y
i
>0;i2ˆ
i
y
i
ˇ
i
D
ˇ
X
y
i
<0;i2ˆ
i
y
i
y
i
D.˛
i
ˇ
i
/=.˛
i
C ˇ
i
/;
˛
ˇ
a
j
˛
j
D
0
˛
C
X
y
j
>0;j 2ˆ
j
y
j
ˇ
j
D
0
ˇ
X
y
j
<0;j 2ˆ
j
y
j
y
j
D.˛
j
ˇ
j
/=.˛
j
C ˇ
j
/;
0
˛
0
ˇ
y
j
implicit
Users News
Fake News
Real News
Probabilistic
Stance
Modeling
News Veracity
Inference
u
i
~ Beta (α
i
, β
i
)
a
j
~ Beta (α
j
, β
j
)
u
u
u
m
a
a
a
N
... ...
Liking
√
?
3.2. POST-BASED DETECTION 35
3.2.2 EMOTION-ENHANCED MODELING
Oh my god!
China ranks second to the
last
most ridiculous seriously?
(a) Emotion in News Content (a) Emotion in User Comments
news content
user comments
36 3. HOW SOCIAL CONTEXT HELPS
n
con com
n
n
n
n
n
h
w
h
n
w
h
w
h
n
w
h
e
h
n
e
h
e
h
n
e
w
w
n
e
e
n
e
e
n
w
w
n
se
Softmax
Gate_M
Gate_N
Content Module Comment Module
Gate_C
Bi-GRU
Bi-GRU Bi-GRU Bi-GRU Bi-GRU
Bi-GRU
··················
······
······ ······ ······ ······
············
Learning Emotion Embeddings
http://www.keenage.com/html/e_index.html
3.2. POST-BASED DETECTION 37
e
i
w
i
Incorporating Emotion Representations
w
i
w
i
!
f
w
0
w
M
f
w
n
w
0
!
h
w
i
D
!
GRU.w
i
/; i 2 Œ0; n;
h
w
i
D
GRU.w
i
/; i 2 Œ0; n:
w
i
h
w
i
!
h
w
i
h
w
i
h
w
i
D Œ
!
h
w
i
;
h
w
i
e
i
h
e
i
w
i
!
h
e
i
D
!
GRU.e
i
/; i 2 Œ0; n;
h
e
i
D
GRU.e
i
/; i 2 Œ0; n;
w
i
h
e
i
!
h
e
i
h
e
i
h
e
i
D Œ
!
h
e
i
;
h
e
i
a
numbers of positive/negative words,
sentiment score a se
forget gate input gate
r
t
u
t
38 3. HOW SOCIAL CONTEXT HELPS
r
t
D .W
r
Œse; h
e
t
C b
r
/
u
t
D .W
u
Œse; h
e
t
C b
u
/
c
e
t
D tanh.W
c
Œse; h
e
t
C b
c
/
n
t
D r
t
ˇ h
w
t
C u
t
ˇ c
e
t
:
update gate
u
t
h
e
t
w
t
n
t
w
t
h
e
t
u
t
D .W
u
Œh
w
t
; h
e
t
C b
u
/
c
e
t
D tanh.W
c
h
e
t
C b
c
/
n
t
D u
t
ˇ h
w
t
C .1 u
t
/ ˇ c
e
t
:
Emotion-Based Fake News Detection
n
r D .W
u
Œcon; com C b
u
/
o D r ˇ con C .1 r/ ˇ com:
n
Oy D .W
f
o C b
f
/;
Oy D ŒOy
0
; Oy
1
Oy
0
Oy
1
y 2 f0; 1g
b
f
2 R
12
L./ D y log.Oy
1
/ .1 y/ log.1 Oy
0
/;
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