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A	user‐centric model	of	voting	intention	from	Social Media
Vasileios	Lampos, Daniel	Preoţiuc‐Pietro & Trevor	Cohn
Computer	Science	Department,	University	of	Sheffield,	UK
1.1K	users
23K words
42K	users
81K words
words
users
		 	
⋯
⋯	
⋮
		
⋮
	
⋯
	
⋮⋮ ⋮
⋮
bi‐linear
bias
voting	intention	% for	
political	party	 during	
time	interval	
regularisation
parameter	for	
word	weights
ℓ , ‐norm
:	 row	of	
RMSE % Method Austria UK
training	set	
benchmark
mean poll 1.851 1.69
Last	poll 1.47 1.723
Linear 1.442 3.067
,	 Bilinear 1.699 1.573
,	
Bilinear
Multi‐task
1.439 1.478
… … ∈
… … ∈
∈ 			 , ∈
∈ 			 ∈
filtering	out words &	users
UK	
3	parties
predictions









CON
LAB
LIB
CON
LAB
LIB
polls







SPÖ
ÖVP
FPÖ
GRÜ
Austria	
4	parties
predictions
SPÖ
ÖVP
FPÖ
GRÜ
polls
:	frequency	of	word for	
user during	time	interval	
parties polls
Bi‐convex iterative	learning
1. Solve
,
⦁
2. Fix	 and	solve	
,
⦁
3. Fix	 and	solve
,
⦁
4. Validate	? Go	to	Step	2	: END
prediction
performance

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Vasileios Lampos - 2013 - A user‐centric model of voting intention from Social Media

  • 1. min , , A user‐centric model of voting intention from Social Media Vasileios Lampos, Daniel Preoţiuc‐Pietro & Trevor Cohn Computer Science Department, University of Sheffield, UK 1.1K users 23K words 42K users 81K words words users ⋯ ⋯ ⋮ ⋮ ⋯ ⋮⋮ ⋮ ⋮ bi‐linear bias voting intention % for political party during time interval regularisation parameter for word weights ℓ , ‐norm : row of RMSE % Method Austria UK training set benchmark mean poll 1.851 1.69 Last poll 1.47 1.723 Linear 1.442 3.067 , Bilinear 1.699 1.573 , Bilinear Multi‐task 1.439 1.478 … … ∈ … … ∈ ∈ , ∈ ∈ ∈ filtering out words & users UK 3 parties predictions CON LAB LIB CON LAB LIB polls SPÖ ÖVP FPÖ GRÜ Austria 4 parties predictions SPÖ ÖVP FPÖ GRÜ polls : frequency of word for user during time interval parties polls Bi‐convex iterative learning 1. Solve , ⦁ 2. Fix and solve , ⦁ 3. Fix and solve , ⦁ 4. Validate ? Go to Step 2 : END prediction performance