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Mining socio-political and socio-economic
signals from social media content
Vasileios Lampos
Department of Computer Scienc...
Structure of the presentation
1. Introductory remarks
2. Collective inference tasks

— Mining emotions

— Modelling voting...
Context and motivation
How can we use online user-generated content to
enhance our understanding about our world?
the Inte...
Context and motivation
How can we use online user-generated content to
enhance our understanding about our world?
the Inte...
About Twitter
About Twitter
> 140 characters per published status (tweet)
> users can follow and be followed
> embedded usage of topics ...
Inferring collective information 

from user-generated content
Lampos (Ph.D. Thesis, 2012)
Lansdall-Welfare, Lampos & Cris...
Emotion taxonomies and quantification
‘Emotional’ keywords, representing
+ anger, e.g. angry, irritate
+ fear, e.g. fearfu...
Emotion taxonomies and quantification
> WordNet Affect
> Linguistic Inquiry and Word Count (LIWC)
(Strapparava & Valitutti...
Circadian emotion patterns from Twitter (UK)
Winter Summer Aggregated Data
SadnessScore
3 6 9 12 15 18 21 24
-0.1
0
0.1
3 ...
‘Joy’ time series based on Twitter (UK)
27august2012
time, in a manner that is partly predictable (or
at least interpretab...
Recession, riots, and Twitter emotions (UK)
Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 12
−1
−0.5
0
0.5
1
1.5
Rate of Mood Cha...
Inferring voting intention — Data sets
+ 3 political parties (Conservatives, Labour, Lib Dem)
+ 42,000 Twitter users distr...
Regularised text regression✓v =
qv q⇤
v
q⇤
v
xi 2 Rm, i 2 {1, . . . , n} — X
yi 2 R, i 2 {1, . . . , n} — y
wj, 2 R, j 2 {...
Regularised text regression✓v =
qv q⇤
v
q⇤
v
xi 2 Rm, i 2 {1, . . . , n} — X
yi 2 R, i 2 {1, . . . , n} — y
wj, 2 R, j 2 {...
Bilinear (users+text) regularised regression
p 2 Z+
Qi 2 Rp⇥m, i 2 {1, . . . , n} — X
yi 2 R, i 2 {1, . . . , n} — y
uk, w...
Bilinear elastic net (BEN)f (QQQi) = uuuTQQQiwww + —
◊ ◊ + —
uuuT QQQi
www
v.lampos@ucl.ac.uk Slides: http://bit.ly/1GrxI8...
Training bilinear elastic net (BEN)
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0
0.4
0.8
1.2
1.6
2
2.4
Step
Global Objective...
Training bilinear elastic net (BEN)
stic Net (BEN)
ÿ 1
uuuT
QQQiwww + — ≠ yi
22
BEN’s objective function
u1 ÎuuuÎ2
¸2
+ ⁄u...
Bilinear and multi-task regression
⌧ 2 Z+
p 2 Z+
Qi 2 Rp⇥m, i 2 {1, . . . , n} — X
yi 2 R⌧ , i 2 {1, . . . , n} — Y
uk, wj...
Bilinear Group L2,1 (BGL)
+ a nonzero weighted feature (user or word) is
encouraged to be nonzero for all tasks, but with
...
Voting intention inference performanceRootMeanSquaredError
0
1
2
2
3
UK Austria
1.4391.478
1.699
1.573
1.442
3.067
1.47
1....
Voting intention inference performanceRootMeanSquaredError
0
1
2
2
3
UK Austria
1.4391.478
1.699
1.573
1.442
3.067
1.47
1....
Voting intention comparative plots
5 10 15 20 25 30 35 40 45
0
5
10
15
20
25
30
35
40
VotingIntention%
Time
CON
LAB
LIB
BE...
Voting intention comparative plots
5 10 15 20 25 30 35 40 45
0
5
10
15
20
25
30
VotingIntention%
Time
SPÖ
ÖVP
FPÖ
GRÜ
Poll...
Qualitative insights
Party Tweet Score User type
SPÖ
centre
Inflation rate in Austria slightly down in
July from 2.2 to 2....
Inferring user-level information 

from user-generated content
Preotiuc-Pietro, Lampos & Aletras (ACL 2015)
Preotiuc-Pietr...
Linguistic expression and demographics
“Socioeconomic variables are influencing language use.”
+ Validate this hypothesis ...
Standard Occupational Classification (SOC)
Major Group 1 (C1): Managers, Directors and Senior Officials
Sub-major Group 11:...
Standard Occupational Classification (SOC)
C1 — Managers, Directors & Senior Officials
(chief executive, bank manager)
C2 ...
Forming a Twitter user data set
+ 5,191 Twitter users mapped to their occupations,
then mapped to one of the 9 SOC categor...
Twitter user attributes (18 in total)
number of
— followers
— friends
— followers/friends (ratio)
— times listed
— tweets
...
Twitter user discussion topics (I)
Topics — Word clusters (#: 30, 50, 100, 200)
+ SVD on the graph laplacian of the word b...
Twitter user discussion topics (II)
Topic Most central words; Most frequent words
Arts archival, stencil, canvas, minimali...
A few words about Gaussian Processes
Why do we use Gaussian Processes?
+ Kernelised, models nonlinearities
+ Interpretabil...
More information about Gaussian Processes
+ Book: “Gaussian Processes for Machine Learning”

http://www.gaussianprocess.or...
Gaussian Process classifierin neu-
ojected
ce via a
models
ntation
models
syntac-
xt, han-
dimen-
nce are
he skip-
ov et a...
Occupation classification performance
Accuracy(%)
25
31
37
43
49
55
User Attributes Topics (SVD) Topics (word2vec)
52.7
48...
Occupation classification performance
Accuracy(%)
25
31
37
43
49
55
User Attributes Topics (SVD) Topics (word2vec)
52.7
48...
Occupation classification performance
Accuracy(%)
25
31
37
43
49
55
User Attributes Topics (SVD) Topics (word2vec)
52.7
48...
Occupation classification insights (I)
0.001 0.01 0.05
0
0.2
0.4
0.6
0.8
1
Topic proportion
Userprobability
Higher Educati...
Occupation classification insights (II)
CDF of the topic “Arts”: Topic more prevalent in C5 (which
includes artists) and t...
Occupation classification insights (II)
CDF of the topic “Arts”: Topic more prevalent in C5 (which
includes artists) and t...
Occupation classification insights (III)
CDF of the topic “Elongated Words”: Topic more prevalent
in the lower classes, an...
Occupation classification insights (III)
CDF of the topic “Elongated Words”: Topic more prevalent
in the lower classes, an...
Occupation classification insights (IV)
Topic distribution distance (Jensen-Shannon divergence)
for the different occupati...
Occupation classification insights (IV)
Topic distribution distance (Jensen-Shannon divergence)
for the different occupati...
Occupation classification insights (IV)
Topic distribution distance (Jensen-Shannon divergence)
for the different occupati...
Occupation classification insights (V)
Health
Beauty Care
Education
Football*
Corporate
Elongated Words
Politics
Topic sco...
Additional ‘perceived’ user features
+ Previously used features: Profile features, Shallow
profile features, and Topics
+ ...
Defining the user income regression task
(e.g. ‘spare time guitarist’), contained multiple possible jobs (e.g. ‘marketer, ...
User income regression: data
Income prediction
10k 30k 50k 100k
0
200
400
600
800
1000
Yearly income (£)
No.Users
We appro...
User income regression performance
MAE
£9,000
£9,500
£10,000
£10,500
£11,000
£11,500
Income inference error (Mean Absolute...
User income regression insights (I)Studying User Income in Social Media
User income regression insights (II)
• Neutral sentiment increases with income, while both positive and negative sentiment...
User income regression insights (III)
e1: positive (l=46.27) e2: neutral (l=57.64) e3: negative(l=76.34)
e4: joy (l=36.37)...
User income regression insights (IV)
Topic 107 (Justice) Topic 124 (Corporate 1) Topic 139 (Politics)
Topic 163 (NGOs) Top...
Defining a user SES classification task
Profile description
on Twitter
Occupation SOC category1 NS-SEC2
1. Standard Occupa...
UK Twitter user data set for SES classification
+ 1,342 UK Twitter user profiles
+ 2 million tweets
+ Date interval: Feb. ...
SES classification performance
Classification Accuracy (%) Precision (%) Recall (%) F1
2 classes 82.05 (2.4) 82.2 (2.4) 81...
Conclusions — Mining socio-political and
socio-economic signals from social media
collective emotion
voting intention
occu...
Further thoughts
+ User-generated content is a valuable asset
+ Nonlinear models tend to perform better given
the multimod...
Some of the future research challenges
+ Work closer with domain experts
+ Better understanding of online media biases,
e....
Acknowledgements
Currently funded by
All collaborators (in alphabetical order)
in research mentioned today
Nikolaos Aletra...
Thank you!
Any questions?
Slides can be downloaded from
lampos.net/talks
@lampos | lampos.net
References
Argyriou, Evgeniou & Pontil. Convex Multi-Task Feature Learning (Machine Learning, 2008)
Bernstein. Language an...
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Mining socio-political and socio-economic signals from social media content

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Over the past few years user-generated content, primarily originating from social media platforms, has been the focus of considerable research effort. One of the main motivations is the potential of using this data as an additional source of information for enhancing our understanding of societal or more personalised
signals. This talk will showcase a series of ideas which explored that basic hypothesis, proposing statistical natural language processing frameworks for the estimation of collective patterns (e.g. emotions or voting intentions) or of more focused demographic user attributes (e.g. the occupation, income and socioeconomic status). Apart from the essential statistical evaluation of the proposed methods, interesting insights, stemming from a qualitative analysis, will be presented as well.

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Mining socio-political and socio-economic signals from social media content

  1. 1. Mining socio-political and socio-economic signals from social media content Vasileios Lampos Department of Computer Science University College London Summer School on “Big Data & Networks in Social Sciences” University of Warwick, Sept. 21-23, 2016 @lampos | lampos.net
  2. 2. Structure of the presentation 1. Introductory remarks 2. Collective inference tasks
 — Mining emotions
 — Modelling voting intention 3. Personalised inference tasks
 — Occupational class
 — Income
 — Socioeconomic status 4. Concluding remarks
  3. 3. Context and motivation How can we use online user-generated content to enhance our understanding about our world? the Internet, the World Wide Web, connectivity numerous web products feeding from user activity user-generated content, publicly available, esp. on social media platforms (e.g. Twitter) large-scale digitised data, ‘Big Data’, ‘Data Science’
  4. 4. Context and motivation How can we use online user-generated content to enhance our understanding about our world? the Internet, the World Wide Web, connectivity numerous web products feeding from user activity user-generated content, publicly available, esp. on social media platforms (e.g. Twitter) large-scale digitised data, ‘Big Data’, ‘Data Science’
  5. 5. About Twitter
  6. 6. About Twitter > 140 characters per published status (tweet) > users can follow and be followed > embedded usage of topics (using #hashtags) > user interaction (re-tweets, @mentions, likes) > real-time nature > biased demographics (13-15% of UK’s population, age bias etc.) > information is noisy and not always accurate
  7. 7. Inferring collective information 
 from user-generated content Lampos (Ph.D. Thesis, 2012) Lansdall-Welfare, Lampos & Cristianini (WWW 2012) Lampos, Preotiuc-Pietro & Cohn (ACL 2013) mood / emotions voting intention
  8. 8. Emotion taxonomies and quantification ‘Emotional’ keywords, representing + anger, e.g. angry, irritate + fear, e.g. fearful, afraid + joy, e.g. cheerful, enthusiastic + sadness, e.g. depressed, gloomy + plus other emotions > WordNet Affect > Linguistic Inquiry and Word Count (LIWC) (Strapparava & Valitutti, 2004; Pennebaker et al., 2001, 2007) Simply — but maybe not good enough! — we compute the mean keyword frequency score per emotion
  9. 9. Emotion taxonomies and quantification > WordNet Affect > Linguistic Inquiry and Word Count (LIWC) (Strapparava & Valitutti, 2004; Pennebaker et al., 2001, 2007) Simply — but maybe not good enough! — we compute the mean keyword frequency score per emotion ‘Emotional’ keywords, representing + anger, e.g. angry, irritate + fear, e.g. fearful, afraid + joy, e.g. cheerful, enthusiastic + sadness, e.g. depressed, gloomy + plus other emotions
  10. 10. Circadian emotion patterns from Twitter (UK) Winter Summer Aggregated Data SadnessScore 3 6 9 12 15 18 21 24 -0.1 0 0.1 3 6 9 12 15 18 21 24 -0.1 0 0.1 JoyScore 3 6 9 12 15 18 21 24 -0.1 0 0.1 3 6 9 12 15 18 21 24 -0.1 0 0.1 Hourly Intervals Hourly Intervals re 1. Plots representing the variation over a 24-hour period of the emotional valen ear, sadness, joy and anger. The red line represents days in the winter, while the green one sents days in the summer. The average circadian pattern was extracted by aggregating the two nal data sets. Faded colourings represent the SE of the sample mean. 24h emotion patterns for ‘joy’ and ‘sadness’ for summer and winter with 95% confidence intervals
  11. 11. ‘Joy’ time series based on Twitter (UK) 27august2012 time, in a manner that is partly predictable (or at least interpretable). We were reassured to find there was a periodic peak of joy around It is interesting that the growth in anger seems to have started before the riots them- selves, but this does not mean that we could Figure 2. Plot of the time series representing levels of joy estimator over a period of 2½ years. Notice the peaks corresponding to Christmas and New Year, Valentine’s day and the Royal Wedding Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 12 −2 0 2 4 6 8 10 933 Day Time Series for Joy in Twitter Content Date NormalisedEmotionalValence * RIOTS * CUTS * XMAS * XMAS * XMAS * roy.wed. * halloween * halloween * halloween * valentine* valentine * easter * easter raw joy signal 14−day smoothed joy Clear peaking pattern during XMAS or other annual celebrations (Valentine’s Day, Easter)
  12. 12. Recession, riots, and Twitter emotions (UK) Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 12 −1 −0.5 0 0.5 1 1.5 Rate of Mood Change by Day using the Difference in 50−day Mean Date Differenceinmean Anger Fear Date of Budget Cuts Date of Riots Riots (UK)Budget Cuts (UK) Difference in mean mood score 50 days prior and after each date; peaks indicate increase in mood change
  13. 13. Inferring voting intention — Data sets + 3 political parties (Conservatives, Labour, Lib Dem) + 42,000 Twitter users distributed proportionally to UK’s regional population figures + 60 million tweets, 80,976 1-grams + 240 polls from 30 Apr. 2010 to 13 Feb. 2012 United Kingdom + 4 political parties (SPO, OVP, FPO, GRU) + 1,100 active Twitter users selected by political scientists + 800,000 tweets, 22,917 1-grams + 98 polls from 25 Jan. to 25 Dec. 2012 Austria
  14. 14. Regularised text regression✓v = qv q⇤ v q⇤ v xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + ✓v = qv qv q⇤ v xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + qv xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + observations responses weights, bias f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + Elastic Net argmin w, 8 < : nX i=1 0 @yi mX j=1 xijwj 1 A 2 + 1 mX j=1 |wj| + 2 mX j=1 w2 j 9 = ; L1-norm L2-norm (Zou & Hastie, 2005)
  15. 15. Regularised text regression✓v = qv q⇤ v q⇤ v xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + ✓v = qv qv q⇤ v xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + qv xi 2 Rm, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y wj, 2 R, j 2 {1, . . . , m} — w⇤ = [w; ] f(xi) = xT i w + observations responses weights, bias f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + Elastic Net ) = xT i w + i) = uTQiw + i) = tr UTQiW + argmin w, 8 < : nX i=1 0 @yi mX j=1 xijwj 1 A 2 + 1 mX j=1 |wj| + 2 mX j=1 w2 j 9 = ; L1-norm L2-norm (Zou & Hastie, 2005)
  16. 16. Bilinear (users+text) regularised regression p 2 Z+ Qi 2 Rp⇥m, i 2 {1, . . . , n} — X yi 2 R, i 2 {1, . . . , n} — y uk, wj, 2 R, k 2 {1, . . . , p} — u, w, j 2 {1, . . . , m} users observations responses weights, bias f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + Bilinear Text Regression — The general idea (2/2) • users p œ Z+ • observations QQQi œ Rp◊m, i œ {1, ..., n} — XXX • responses yi œ R, i œ {1, ..., n} — yyy • weights, bias uk, wj, — œ R, k œ {1, ..., p} — uuu, www, — j œ {1, ..., m} f (QQQi) = uuuTQQQiwww + — ◊ ◊ + — uuuT QQQi www f(xi) = xT i w + f (Qi) = uTQiw + f(xi) = xT i w + f (Qi) = uTQiw + f(xi) = xT i w + f (Qi) = uTQiw +
  17. 17. Bilinear elastic net (BEN)f (QQQi) = uuuTQQQiwww + — ◊ ◊ + — uuuT QQQi www v.lampos@ucl.ac.uk Slides: http://bit.ly/1GrxI8j 16/45 16 /4 f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + where = tr UTQiW + argmin w, 8 < : nX i=1 0 @yi mX j=1 xijwj 1 A 2 + 1 mX j=1 |wj| + 2 mX j=1 w2 j 9 = ; argmin u,w, ( nX i=1 uT Qiw + yi 2 + (u, ✓u) + (w, ✓w) ) (x, 1, 2) = 1kxk`1 + 2kxk2 `2 = uTQiw + = tr UTQiW + argmin w, 8 < : nX i=1 0 @yi mX j=1 xijwj 1 A 2 + 1 mX j=1 |wj| + 2 mX j=1 w2 j 9 = ; argmin u,w, ( nX i=1 uT Qiw + yi 2 + (u, ✓u) + (w, ✓w) ) (x, 1, 2) = 1kxk`1 + 2kxk2 `2
  18. 18. Training bilinear elastic net (BEN) 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 0.4 0.8 1.2 1.6 2 2.4 Step Global Objective RMSE Global objective function during training (red) Corresponding prediction error on held out data (blue) Biconvex problem + fix u, learn w and vice versa + iterate through convex optimisation tasks (Mairal et al., 2010)Large-scale solvers in SPAMS argmin w, : i=1 @yi j=1 xijwjA + 1 j=1 |wj| + 2 j=1 w2 j ; argmin u,w, ( nX i=1 uT Qiw + yi 2 + (u, ✓u) + (w, ✓w) ) (x, 1, 2) = 1kxk`1 + 2kxk2 `2
  19. 19. Training bilinear elastic net (BEN) stic Net (BEN) ÿ 1 uuuT QQQiwww + — ≠ yi 22 BEN’s objective function u1 ÎuuuÎ2 ¸2 + ⁄u2 Îuuuθ1 w1 ÎwwwÎ2 ¸2 + ⁄w2 Îwwwθ1 J 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 0.4 0.8 1.2 1.6 2 2.4 Step Global Objective RMSE xity: fix uuu, learn www and vv through convex on tasks: convergence Global objective function during training (red) Corresponding prediction error on held out data (blue) Biconvex problem + fix u, learn w and vice versa + iterate through convex optimisation tasks (Mairal et al., 2010)Large-scale solvers in SPAMS argmin w, : i=1 @yi j=1 xijwjA + 1 j=1 |wj| + 2 j=1 w2 j ; argmin u,w, ( nX i=1 uT Qiw + yi 2 + (u, ✓u) + (w, ✓w) ) (x, 1, 2) = 1kxk`1 + 2kxk2 `2
  20. 20. Bilinear and multi-task regression ⌧ 2 Z+ p 2 Z+ Qi 2 Rp⇥m, i 2 {1, . . . , n} — X yi 2 R⌧ , i 2 {1, . . . , n} — Y uk, wj, 2 R⌧ , k 2 {1, . . . , p} — U, W, j 2 {1, . . . , m} tasks users observations responses weights, bias f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + Bilinear Multi-Task Learning • tasks · œ Z+ • users p œ Z+ • observations QQQi œ Rp◊m, i œ {1, ..., n} — XXX • responses yyyi œ R· , i œ {1, ..., n} — YYY • weights, bias uuuk,wwwj,——— œ R· , k œ {1, ..., p} — UUU, WWW, ——— j œ {1, ..., m} f (QQQi) = tr 1 UUUTQQQiWWW 2 + ——— ◊ ◊ UUUT QQQi WWW f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + f(xi) = xT i w + f (Qi) = uTQiw + f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW +
  21. 21. Bilinear Group L2,1 (BGL) + a nonzero weighted feature (user or word) is encouraged to be nonzero for all tasks, but with potentially different weights + intuitive for political preference inference f (QQQi) = tr 1 UUUTQQQiWWW 2 + ——— ◊ ◊ UUUT QQQi WWW v.lampos@ucl.ac.uk Slides: http://bit.ly/1GrxI8j 23/45 23 /45 f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + f(xi) = xT i w + f (Qi) = uTQiw + f (Qi) = tr UTQiW + argmin w, : i=1 @yi j=1 xijwjA + 1 j=1 |wj| + 2 j=1 wj ; argmin u,w, ( nX i=1 uT Qiw + yi 2 + (u, ✓u) + (w, ✓w) ) (x, 1, 2) = 1kxk`1 + 2kxk2 `2 argmin U,W, 8 < : ⌧X t=1 nX i=1 uT Qiwt + t yti 2 + u pX k=1 kUkk2 + w mX j=1 kWjk2 9 = ;
  22. 22. Voting intention inference performanceRootMeanSquaredError 0 1 2 2 3 UK Austria 1.4391.478 1.699 1.573 1.442 3.067 1.47 1.723 1.851 1.69 Mean poll Last poll Elastic Net (words) BEN BGL
  23. 23. Voting intention inference performanceRootMeanSquaredError 0 1 2 2 3 UK Austria 1.4391.478 1.699 1.573 1.442 3.067 1.47 1.723 1.851 1.69 Mean poll Last poll Elastic Net (words) BEN BGL
  24. 24. Voting intention comparative plots 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 VotingIntention% Time CON LAB LIB BEN 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 VotingIntention% Time CON LAB LIBBGL 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 VotingIntention% Time CON LAB LIBYouGov
  25. 25. Voting intention comparative plots 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 VotingIntention% Time SPÖ ÖVP FPÖ GRÜ Polls 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 VotingIntention% Time SPÖ ÖVP FPÖ GRÜ BEN 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 VotingIntention% Time SPÖ ÖVP FPÖ GRÜ BGL
  26. 26. Qualitative insights Party Tweet Score User type SPÖ centre Inflation rate in Austria slightly down in July from 2.2 to 2.1%. Accommodation, Water, Energy more expensive. 0.745 Journalist ÖVP centre right Can really recommend the book “Res Publica” by Johannes #Voggenhuber! Food for thought and so on #Europe #Democracy -2.323 Normal user FPÖ
 far right Campaign of the Viennese SPO on “Living together” plays right into the hands of right- wing populists -3.44 Human rights GRÜ centre left Protest songs against the closing-down of the bachelor course of International Development: <link> #ID_remains #UniBurns #UniRage 1.45 Student Union
  27. 27. Inferring user-level information 
 from user-generated content Preotiuc-Pietro, Lampos & Aletras (ACL 2015) Preotiuc-Pietro, Volkova, Lampos, Bachrach & Aletras (PLOS ONE, 2015) Lampos, Aletras, Geyti, Zou & Cox (ECIR 2016) occupational class income socio-economic status (SES)
  28. 28. Linguistic expression and demographics “Socioeconomic variables are influencing language use.” + Validate this hypothesis on a broader, larger data set using social media + Applications > research, as in computational social science, health, and psychology > commercial (Bernstein, 1960; Labov, 1972/2006)
  29. 29. Standard Occupational Classification (SOC) Major Group 1 (C1): Managers, Directors and Senior Officials Sub-major Group 11: Corporate Managers and Directors Minor Group 111: Chief Executives and Senior Officials Unit Group 1115: Chief Executives and Senior Officials •Job: chief executive, bank manager Unit Group 1116: Elected Officers and Representatives Minor Group 112: Production Managers and Directors Minor Group 113: Functional Managers and Directors Minor Group 115: Financial Institution Managers and Directors Minor Group 116: Managers and Directors in Transport and Logistics Minor Group 117: Senior Officers in Protective Services Minor Group 118: Health and Social Services Managers and Directors Minor Group 119: Managers and Directors in Retail and Wholesale Sub-major Group 12: Other Managers and Proprietors Major Group (C2): Professional Occupations •Job: mechanical engineer, pediatrist Major Group (C3): Associate Professional and Technical Occupations •Job: system administrator, dispensing optician Major Group (C4): Administrative and Secretarial Occupations •Job: legal clerk, company secretary Major Group (C5): Skilled Trades Occupations •Job: electrical fitter, tailor Major Group (C6): Caring, Leisure and Other Service Occupations •Job: nursery assistant, hairdresser Major Group (C7): Sales and Customer Service Occupations •Job: sales assistant, telephonist Major Group (C8): Process, Plant and Machine Operatives •Job: factory worker, van driver Major Group (C9): Elementary Occupations •Job: shelf stacker, bartender 9 major groups 25 sub-major groups 90 minor groups 369 unit groups provided by the Office for National Statistics (UK)
  30. 30. Standard Occupational Classification (SOC) C1 — Managers, Directors & Senior Officials (chief executive, bank manager) C2 — Professional Occupations (postdoc, pediatrist) C3 — Associate Professional & Technical (system administrator, dispensing optician) C4 — Administrative & Secretarial (legal clerk, secretary) C5 — Skilled Trades (electrical fitter, tailor) C6 — Caring, Leisure, Other Service (nursery assistant, hairdresser) C7 — Sales & Customer Service (sales assistant, telephonist) C8 — Process, Plant and Machine Operatives (factory worker, van driver) C9 — Elementary (shelf stacker, bartender) The 9 major occupational classes (C1-9)
  31. 31. Forming a Twitter user data set + 5,191 Twitter users mapped to their occupations, then mapped to one of the 9 SOC categories + 10 million tweets + Download the data set % of users per SOC category 0 7 14 21 28 35 C1 C2 C3 C4 C5 C6 C7 C8 C9
  32. 32. Twitter user attributes (18 in total) number of — followers — friends — followers/friends (ratio) — times listed — tweets — favourites (likes) — unique @-mentions — tweets/day (avg.) — retweets/tweet (avg.) proportion of — retweets done — non duplicate tweets — retweeted tweets — hashtags — tweets with hashtags — tweets with @-mentions — @-replies — tweets with links — tweets in English Similarly to our paper for user impact estimation (Lampos et al., 2014)
  33. 33. Twitter user discussion topics (I) Topics — Word clusters (#: 30, 50, 100, 200) + SVD on the graph laplacian of the word by word similarity matrix using normalised PMI, i.e. a form of spectral clustering + Word2vec (skip-gram with negative sampling) to learn word embeddings; pairwise cosine similarity on the embeddings to derive a word by word similarity matrix; then spectral clustering on the similarity matrix (Bouma, 2009; von Luxburg, 2007) (Mikolov et al., 2013)
  34. 34. Twitter user discussion topics (II) Topic Most central words; Most frequent words Arts archival, stencil, canvas, minimalist; art, design, print Health chemotherapy, diagnosis, disease; risk, cancer, mental, stress Beauty Care exfoliating, cleanser, hydrating; beauty, natural, dry, skin Higher Education undergraduate, doctoral, academic, students, curriculum; students, research, board, student, college, education, library Football bardsley, etherington, gallas; van, foster, cole, winger Corporate consortium, institutional, firm’s; patent, industry, reports Elongated Words yaaayy, wooooo, woooo, yayyyyy, yaaaaay, yayayaya, yayy; wait, till, til, yay, ahhh, hoo, woo, woot, whoop, woohoo Politics religious, colonialism, christianity, judaism, persecution, fascism, marxism; human, culture, justice, religion, democracy
  35. 35. A few words about Gaussian Processes Why do we use Gaussian Processes? + Kernelised, models nonlinearities + Interpretability (AutoRelevance Determination) + Performance re- he 2) rd. eu- ed a a els on Formally, GP methods aim to learn a function f : Rd ! R drawn from a GP prior given the inputs xxx 2 Rd: f(xxx) ⇠ GP(m(xxx), k(xxx,xxx0 )) , (3) where m(·) is the mean function (here 0) and k(·, ·) is the covariance kernel. Usually, the Squared Ex- ponential (SE) kernel (a.k.a. RBF or Gaussian) is used to encourage smooth functions. For the multi- dimensional pair of inputs (xxx,xxx0), this is: kard(xxx,xxx0 ) = 2 exp " dX i (xi x0 i)2 2l2 i # , (4) on, while the clusters can be interpreted ing a list of the most frequent or repre- words. The latter are identified using the centrality metric: Cw = P x2c NPMI(w, x) |c| 1 , (2) enotes the cluster and w the target word. ural Embeddings (W2V-E) there has been a growing interest in neu- ge models, where the words are projected er dimensional dense vector space via a er (Mikolov et al., 2013b). These models and Cohn, 2013) with limited to (Polajnar et Formally, GP meth f : Rd ! R drawn inputs xxx 2 Rd: f(xxx) ⇠ GP where m(·) is the mean is the covariance kern ponential (SE) kernel used to encourage smo dimensional pair of in kard(xxx,xxx0 ) = 2 exp e- he 2) d. u- d a ls n Formally, GP methods aim to learn a function f : Rd ! R drawn from a GP prior given the inputs xxx 2 Rd: f(xxx) ⇠ GP(m(xxx), k(xxx,xxx0 )) , (3) where m(·) is the mean function (here 0) and k(·, ·) is the covariance kernel. Usually, the Squared Ex- ponential (SE) kernel (a.k.a. RBF or Gaussian) is used to encourage smooth functions. For the multi- dimensional pair of inputs (xxx,xxx0), this is: kard(xxx,xxx0 ) = 2 exp " dX i (xi x0 i)2 2l2 i # , (4) where li are lengthscale parameters learnt only Say and we want to learn Formally: Sets of random variables any finite number of which have a multivariate Gaussian distribution mean function drawn on inputs covariance function (kernel) drawn on pairs of inputs (Rasmussen & Williams, 2006)
  36. 36. More information about Gaussian Processes + Book: “Gaussian Processes for Machine Learning”
 http://www.gaussianprocess.org/gpml/ + Video-lecture: “Gaussian Process Basics”
 http://videolectures.net/gpip06_mackay_gpb/ + Tutorial tailored to statistical NLP tasks: “Gaussian Processes for Natural Language Processing”
 http://people.eng.unimelb.edu.au/tcohn/tutorial.html + Software I — GPML for Octave or MATLAB
 http://www.gaussianprocess.org/gpml/code + Software II — GPy for Python
 http://sheffieldml.github.io/GPy/
  37. 37. Gaussian Process classifierin neu- ojected ce via a models ntation models syntac- xt, han- dimen- nce are he skip- ov et al., Twitter m model evy and used to encourage smooth functions. For the multi- dimensional pair of inputs (xxx,xxx0), this is: kard(xxx,xxx0 ) = 2 exp " dX i (xi x0 i)2 2l2 i # , (4) where li are lengthscale parameters learnt only using training data by performing gradient as- cent on the type-II marginal likelihood. Intuitively, the lengthscale parameter li controls the variation along the i input dimension, i.e. a low value makes the output very sensitive to input data, thus mak- ing that input more useful for the prediction. If the lengthscales are learnt separately for each input dimension the kernel is named SE with Automatic Relevance Determination (ARD) (Neal, 1996). + Squared-exponential ARD covariance function: determines (quantify) the relevancy of each user feature, i.e. the relevance of feature i is inversely proportional to the length-scale hyper-parameter li + 9-class classification using one vs. all + GP hyper-parameter learning with Expectation 
 Propagation + Inference using FITC (500 inducing points)
  38. 38. Occupation classification performance Accuracy(%) 25 31 37 43 49 55 User Attributes Topics (SVD) Topics (word2vec) 52.7 48.2 34.2 51.7 47.9 31.5 46.9 44.2 34 Logistic Regression SVM (RBF) Gaussian Process (SE-ARD) most frequent class baseline (34.4%)
  39. 39. Occupation classification performance Accuracy(%) 25 31 37 43 49 55 User Attributes Topics (SVD) Topics (word2vec) 52.7 48.2 34.2 51.7 47.9 31.5 46.9 44.2 34 Logistic Regression SVM (RBF) Gaussian Process (SE-ARD) most frequent class baseline (34.4%)
  40. 40. Occupation classification performance Accuracy(%) 25 31 37 43 49 55 User Attributes Topics (SVD) Topics (word2vec) 52.7 48.2 34.2 51.7 47.9 31.5 46.9 44.2 34 Logistic Regression SVM (RBF) Gaussian Process (SE-ARD) most frequent class baseline (34.4%)
  41. 41. Occupation classification insights (I) 0.001 0.01 0.05 0 0.2 0.4 0.6 0.8 1 Topic proportion Userprobability Higher Education (#21) C1 C2 C3 C4 C5 C6 C7 C8 C9 Topic more prevalent ! CDF line closer to bottom-right corner CDF of the topic “Higher Education”: Topic more prevalent in the upper classes (C2, which includes education professionals, and C1), and less so in the lower classes
  42. 42. Occupation classification insights (II) CDF of the topic “Arts”: Topic more prevalent in C5 (which includes artists) and the upper classes 0.001 0.01 0.05 0 0.2 0.4 0.6 0.8 1 Topic proportion Userprobability Arts (#116) C1 C2 C3 C4 C5 C6 C7 C8 C9 Topic more prevalent ! CDF line closer to bottom-right corner
  43. 43. Occupation classification insights (II) CDF of the topic “Arts”: Topic more prevalent in C5 (which includes artists) and the upper classes 0.001 0.01 0.05 0 0.2 0.4 0.6 0.8 1 Topic proportion Userprobability Arts (#116) C1 C2 C3 C4 C5 C6 C7 C8 C9 Topic more prevalent ! CDF line closer to bottom-right corner
  44. 44. Occupation classification insights (III) CDF of the topic “Elongated Words”: Topic more prevalent in the lower classes, and less so in the upper classes 0.001 0.01 0.05 0 0.2 0.4 0.6 0.8 1 Topic proportion Userprobability Elongated Words (#164) C1 C2 C3 C4 C5 C6 C7 C8 C9 Topic more prevalent ! CDF line closer to bottom-right corner
  45. 45. Occupation classification insights (III) CDF of the topic “Elongated Words”: Topic more prevalent in the lower classes, and less so in the upper classes 0.001 0.01 0.05 0 0.2 0.4 0.6 0.8 1 Topic proportion Userprobability Elongated Words (#164) C1 C2 C3 C4 C5 C6 C7 C8 C9 Topic more prevalent ! CDF line closer to bottom-right corner
  46. 46. Occupation classification insights (IV) Topic distribution distance (Jensen-Shannon divergence) for the different occupational classes (1-9) Higher Ed- l as topics ‘Football’ re’ (#153). cales only iscrimina- hich topic formance. ge across cs are cov- shows the Fs) across r these six ers having tweets. A CDF line the plot. evalent in similar pattern in both topics by which users with lower skilled jobs tweet more often. Figure 3: Jensen-Shannon divergence in the topic distributions between the different occupational classes (C 1–9). Occupational Class OccupationalClass
  47. 47. Occupation classification insights (IV) Topic distribution distance (Jensen-Shannon divergence) for the different occupational classes (1-9) Higher Ed- l as topics ‘Football’ re’ (#153). cales only iscrimina- hich topic formance. ge across cs are cov- shows the Fs) across r these six ers having tweets. A CDF line the plot. evalent in similar pattern in both topics by which users with lower skilled jobs tweet more often. Figure 3: Jensen-Shannon divergence in the topic distributions between the different occupational classes (C 1–9). Occupational Class OccupationalClass
  48. 48. Occupation classification insights (IV) Topic distribution distance (Jensen-Shannon divergence) for the different occupational classes (1-9) Occupational Class OccupationalClass Higher Ed- l as topics ‘Football’ re’ (#153). cales only iscrimina- hich topic formance. ge across cs are cov- shows the Fs) across r these six ers having tweets. A CDF line the plot. evalent in similar pattern in both topics by which users with lower skilled jobs tweet more often. Figure 3: Jensen-Shannon divergence in the topic distributions between the different occupational classes (C 1–9).
  49. 49. Occupation classification insights (V) Health Beauty Care Education Football* Corporate Elongated Words Politics Topic scores for occupational class supersets 1.06 3.78 1.41 1.04 2.56 2.24 2.13 2.14 1.9 5.15 1.08 6.04 1.4 4.45 Classes 1-2 Classes 6-9 * times 2 for visualisation purposes
  50. 50. Additional ‘perceived’ user features + Previously used features: Profile features, Shallow profile features, and Topics + Based on the work of Volkova et al. (2015), we also incorporated: > Inferred Psycho-Demographic features (15)
 e.g. gender, age, education level, religion, life satisfaction, excitement, anxiety etc. > Emotions (9)
 e.g. positive / negative sentiment, joy, anger, fear, disgust, sadness, surprise etc.
  51. 51. Defining the user income regression task (e.g. ‘spare time guitarist’), contained multiple possible jobs (e.g. ‘marketer, social media ana- lyst’) or represented an institutional account (e.g. ‘limo driver company’). In total, around 50% Table 1. Subset of the SOC classification hierarchy. Group 112: Production Managers and Directors (50,952 GBP/year) •Job titles: engineering manager, managing director, production manager, construction manager, quarry manager, operations manager Group 241: Conservation and Environment Professionals (53,679 GBP/year) •Job titles: conservation officer, ecologist, energy conservation officer, heritage manager, marine conservationist, energy manager, environmental consultant, environmental engineer, environmental protection officer, environmental scientist, landfill engineer Group 312: Draughtspersons and Related Architectural Technicians (29,167 GBP/year) •Job titles: architectural assistant, architectural, technician, construction planner, planning enforcement officer, cartographer, draughtsman, CAD operator Group 411: Administrative Occupations: Government and Related Organisations (20,373 GBP/year) •Job titles: administrative assistant, civil servant, government clerk, revenue officer, benefits assistant, trade union official, research association secretary Group 541: Textiles and Garments Trades (18,986 GBP/year) •Job titles: knitter, weaver, carpet weaver, curtain maker, upholsterer, curtain fitter, cobbler, leather worker, shoe machinist, shoe repairer, hosiery cutter, dressmaker, fabric cutter, tailor, tailoress, clothing manufacturer, embroiderer, hand sewer, sail maker, upholstery cutter Group 622: Hairdressers and Related Services (10,793 GBP/year) •Job titles: barber, colourist, hair stylist, hairdresser, beautician, beauty therapist, nail technician, tattooist Group 713: Sales Supervisors (18,383 GBP/year) •Job titles: sales supervisor, section manager, shop supervisor, retail supervisor, retail team leader Group 813: Assemblers and Routine Operatives (22,491 GBP/year) •Job titles: assembler, line operator, solderer, quality assurance inspector, quality auditor, quality controller, quality inspector, test engineer, weightbridge operator, type technician Group 913: Elementary Process Plant Occupations (17,902 GBP/year) •Job titles: factory cleaner, hygene operator, industrial cleaner, paint filler, packaging operator, material handler, packer doi:10.1371/journal.pone.0138717.t001 Same Twitter data set as in the job classification task Use an income mapping from SOC to create real-valued target data for the regression task
  52. 52. User income regression: data Income prediction 10k 30k 50k 100k 0 200 400 600 800 1000 Yearly income (£) No.Users We approach the task as regression. + 5,191 Twitter users mapped to their occupations, then mapped to an average income in GBP (£) using the SOC taxonomy + ~11 million tweets + Download the data
  53. 53. User income regression performance MAE £9,000 £9,500 £10,000 £10,500 £11,000 £11,500 Income inference error (Mean Absolute Error) using GP regression or a linear ensemble for all features Feature Categories £9,535 £9,621 £11,456 £10,980 £10,110 £11,291 Profile Demo Emotions Shallow Topics All features
  54. 54. User income regression insights (I)Studying User Income in Social Media
  55. 55. User income regression insights (II) • Neutral sentiment increases with income, while both positive and negative sentiment decrease. This uncovers that lower income users express more subjectivity online; • Anger and fear emotions are more present in users with higher income while sadness, sur- Fig 3. Linear and non-linear (GP) fit for Profile features. Variation of income as a function of user profile features. Linear fit in red, non-linear Gaussian Process fit in black. Brackets show the GP lengthscales—the lower the value, the more important the feature is for prediction. doi:10.1371/journal.pone.0138717.g003 Studying User Income in Social Media Relating income and user attributes Linear vs GP fit
  56. 56. User income regression insights (III) e1: positive (l=46.27) e2: neutral (l=57.64) e3: negative(l=76.34) e4: joy (l=36.37) e5: sadness (l=67.05) e6: disgust (l=116.66) e7: anger (l=95.50) e8: surprise (l=83.61) e9: fear (l=31.74) 28000 35000 42000 28000 35000 42000 28000 35000 42000 0.1 0.2 0.3 0.4 0.5 0.4 0.5 0.6 0.7 0.8 0.9 0.05 0.10 0.15 0.20 0.5 0.6 0.7 0.8 0.05 0.10 0.010 0.015 0.020 0.025 0.030 0.01 0.02 0.03 0.04 0.05 0.10 0.15 0.20 0.25 0.05 0.10 0.15 Feature value Income Relating income and emotion Linear vs GP fit
  57. 57. User income regression insights (IV) Topic 107 (Justice) Topic 124 (Corporate 1) Topic 139 (Politics) Topic 163 (NGOs) Topic 196 (Web analytics/Surveys) Topic 99 (Swearing) 30000 40000 50000 30000 40000 50000 0.00 0.02 0.04 0.06 0.00 0.02 0.04 0.000 0.025 0.050 0.075 0.000 0.025 0.050 0.075 0.100 0.00 0.01 0.02 0.03 0.04 0.00 0.03 0.06 0.09 0.12 Feature value Income Relating income and topics of discussion Linear vs GP fit
  58. 58. Defining a user SES classification task Profile description on Twitter Occupation SOC category1 NS-SEC2 1. Standard Occupational Classification job groups 2. National Statistics Socio-Economic Classification: Map from the job groups in the SOC to a socioeconomic status (SES): upper, middle or lower
  59. 59. UK Twitter user data set for SES classification + 1,342 UK Twitter user profiles + 2 million tweets + Date interval: Feb. 1, 2014 to March 21, 2015 + Labelled with a socioeconomic status (SES), using the occupational class proxy from SOC and NS-SEC: upper, middle, or lower + 1,291 user features following the previous paradigms, i.e. quantifying behaviour, impact, profile info, text in tweets and topics from tweets + Download the data set
  60. 60. SES classification performance Classification Accuracy (%) Precision (%) Recall (%) F1 2 classes 82.05 (2.4) 82.2 (2.4) 81.97 (2.6) .821 (.03) 3 classes 75.09 (3.3) 72.04 (4.4) 70.76 (5.7) .714 (.05) … using a Gaussian Process classifier T1 T2 T3 P O1 606 84 53 81.6% O2 49 186 45 66.4% O3 55 48 216 67.7% R 854% 58.5% 68.8% 75.1% 3-class classification T1 T2 P O1 584 115 83.5% O2 126 517 80.4% R 82.3% 81.8% 82.0% middle & lower merged
  61. 61. Conclusions — Mining socio-political and socio-economic signals from social media collective emotion voting intention occupational class income socio-economic status
  62. 62. Further thoughts + User-generated content is a valuable asset + Nonlinear models tend to perform better given the multimodality of the feature space + Deeper representations of text tend to improve performance + Qualitative analysis is important > Evaluation > Interesting insights
  63. 63. Some of the future research challenges + Work closer with domain experts + Better understanding of online media biases, e.g. demographics, external influence etc. + Generalisation, defining limitations, more rigorous evaluation frameworks + Methodological improvements + Ethical concerns
  64. 64. Acknowledgements Currently funded by All collaborators (in alphabetical order) in research mentioned today Nikolaos Aletras (Amazon)
 Yoram Bachrach (Microsoft Research) 
 Trevor Cohn (University of Melbourne)
 Ingemar J. Cox (UCL)
 Nello Cristianini (University of Bristol) Daniel Preotiuc-Pietro (Penn) Thomas Lansdall-Welfare (University of Bristol) Svitlana Volkova (PNNL) Bin Zou (UCL)
  65. 65. Thank you! Any questions? Slides can be downloaded from lampos.net/talks @lampos | lampos.net
  66. 66. References Argyriou, Evgeniou & Pontil. Convex Multi-Task Feature Learning (Machine Learning, 2008) Bernstein. Language and social class (Br J Sociol, 1960) Bouma. Normalized (pointwise) mutual information in collocation extraction (GSCL, 2009) Labov. The Social Stratification of English in New York City (Cambridge Univ Press, 1972; 2006, 2nd ed.) Lampos. Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods (Ph.D. Thesis, University of Bristol, 2012) Lampos, Aletras, Geyti, Zou & Cox. Inferring the Socioeconomic Status of Social Media Users based on Behaviour and Language (ECIR, 2016) Lampos, Preotiuc-Pietro, Aletras & Cohn. Predicting and Characterising User Impact on Twitter (EACL, 2014) Lampos, Preotiuc-Pietro & Cohn. A user-centric of voting intention from Social Media (ACL, 2013) Lansdall-Welfare, Lampos & Cristianini. Effects of the Recession on Public Mood in the UK (WWW, 2012) Mairal, Jenatton, Obozinski & Bach. Network Flow Algorithms for Structured Sparsity (NIPS, 2010) Mikolov, Chen, Corrado & Dean. Efficient estimation of word representations in vector space (ICLR, 2013) Pennebaker, Booth & Francis. Linguistic Inquiry and Word Count: LIWC2007 (Tech. Report, 2001, 2007) Preotiuc-Pietro, Lampos & Aletras. An analysis of the user occupational class through Twitter content (ACL, 2015) Preotiuc-Pietro, Volkova, Lampos, Bachrach & Aletras. Studying User Income through Language, Behaviour and Affect in Social Media (PLoS ONE, 2015) Rasmussen & Williams. Gaussian Processes for Machine Learning (MIT Press, 2006) Strapparava & Valitutti. WordNet-Affect: An affective extension of WordNet. LREC, 2004. Volkova, Bachrach, Armstrong & Sharma. Inferring Latent User Properties from Texts Published in Social Media (AAAI, 2015) von Luxburg. A tutorial on spectral clustering (Stat Comput, 2007) Zou & Hastie. Regularization and variable selection via the elastic net (J R Stat Soc Series B Stat Methodol, 2005)

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