Predicting user demographics in social networks - Invited Talk at University of Warwick

Predicting User Demographics in Social

NetworksNikolaos Aletras

Amazon [email protected]

mailto:[email protected]

introduction

Motivation• The World Wide Web, social networks and smart

devices

• Vast amounts of user-generated content available online such as social media posts, photos, query logs etc.

• Big Data and Data Science era

• Can we use these data for social good?

Why is it useful?• Enable large scale studies in social sciences

• analyse human behaviour on a large scale

• computational social science

• Tackle real world problems

• education

• health intervention/surveillance

• economic development

Why is it useful?• More intelligent recommender systems

• promotion of public policies

• targeted online political campaigns

• targeted advertising

• Integration to other predictive tasks

• voting intention

• sentiment analysis

• health (e.g. infectious disease outbreak prediction)

What is it?• Utilise user-generated content to infer attributes

(user profiling)

• Examples of user attributes :

• location (Cheng et al., 2010), gender (Burger et al., 2011), impact (Lampos et al., 2014), age (Rao et al., 2010)

• Such tasks are (usually) framed as predictive tasks, i.e. classification or regression

Socioeconomic attributes• Social status influences language use (Bernstein, 1960; Bernstein,

2003; Labov, 2006)

• Hypothesis:

• Language use in Twitter can be indicative of user demographics

• Attributes:

• Occupational class

• Income

• Socioeconomic class

data

SOC taxonomy

• Standard Occupational Classification (SOC):

• Taxonomy of jobs, grouped by skill requirements

• Developed by the UK Office for National Statistics

SOC taxonomy• C1 Corporate Managers and Directors —> chief executive, bank manager

• C2 Professional Occupations —> mechanical engineer, pediatrist, research scientist

• C3 Associate Professional and Technical Occupations —> system administrator, dispensing optician

• C4 Administrative and Secretarial Occupations —> legal clerk, company secretary

• C5 Skilled Trades Occupations —> electrical fitter, tailor

• C6 Caring, Leisure, Other Service Occupations —> school assistant, hairdresser

• C7 Sales and Customer Service Occupations —> sales assistant, telephonist

• C8 Process, Plant and Machine Operatives —> factory worker, van driver

• C9 Elementary Occupations —> shelf stacker, bartender

Map users to their socioeconomic attributes

• Use job titles from SOC to retrieve Twitter accounts

• Read the profile info and/or tweets

• Remove organisations/companies

• Keep only users that annotators agree they belong to a specific class


• Use the SOC class as a proxy to find user’s mean income and socioeconomic class

• Annual Survey of Hours & Earnings + SOC —> Mean income in £

• Production manager (£50,952/year)

• Sales Supervisor (£18,383/year)


• Use the SOC class as a proxy to find user’s mean income and socioeconomic class

• Socioeconomic coding + SOC —> Socioeconomic class (upper, medium, lower)

• Bank manager —> upper

• Government clerk —> medium

• Factory cleaner —> lower

Data sets• Data Set 1

• 5,191 Twitter users - SOC class - Mean income

• 10M tweets (maximum 200 tweets/user)

• Publicly available

• Data Set 2

• 1,342 Twitter users - SOC class - Socioeconomic class

• 2M tweets

• Publicly available

models

Supervised Models

• Traditional linear models (e.g. logistic regression)

• Suport Vector Machines (SVMs)

• Gaussian Processes (GPs)

• Deep Neural Nets

Supervised Models

• We look for two main characteristics:

• Model non-linearities

• Interpretability

Gaussian Processes (GPs)• Bayesian

• kernel-based

• non-parametric

• non-linear

• modelling uncertainty

• interpretable models

Gaussian Processes (GPs)

x 2 Rd

f : Rd ! R

f(x) ⇠ GP(m(x), k(x,x0))

d-dimensional input data

learn a function

mean functiondrawn on inputs

covariance function (kernel)drawn on pairs of inputs

Gaussian Processes (GPs)f(x) ⇠ GP(m(x), k(x,x0))

covariance function (kernel)

Automatical Relevance Determination (ARD) kernel

(Neil, 1996)kard(x,x

0) = �

2exp

h dX

i

� (xi � x

0i)

2

2l2

i

ARD kernel learns feature importance using the lengthscales l —> most discriminative between classes

GPs for Classification

• Pass function through a logistic function to squash the input from to obtain probability:(�1,+1)

⇡(x) = p(yi = 1kfi)

• Inference using Expectation Propagation

Gaussian Processes (GPs)

• More information about GPs:

• http://www.gaussianprocess.org/

http://www.gaussianprocess.org/

Predicting the occupational class

Preotiuc-Pietro et al. (2015, ACL)

Predicting the socioeconomic class

Users Feature vectors GPs SOC class

C1 - C9

• 5,191 users mapped to a SOC class

• ~10M tweets


46.9

51.7 52.7

0%

10%

20%

30%

40%

C1 C2 C3 C4 C5 C6 C7 C8 C9

Distribution of users in the 9 SOC classes


C2 Professional Occupations


Predicting the occupational class: Features

• User profile (18)

• number of followers/friends/listings/tweets

• proportion of retweets/hashtags/@-replies/links

• average of tweets a day/retweets per tweet

• Topics - Word Clusters (200)

• Spectral clustering on a word similarity matrix.

• Words represented as Word2Vec embeddings (Mikolov et al., 2013).

• Similarity is computed as the cosine of the word embeddings.

Predicting the occupational class: Performance

Accu

racy

(%)

0

15

30

45

60

Feature Type

User Profile Word2Vec Clusters

52.7

34.2

51.7

31.5

46.9

34

Logistic Regression SVM (RBF) Gaussian Process (ARD)

Predicting the occupational class: Topics

Rank Label Topic

1 Arts art, design, print, collection, poster, painting, custom, logo, printing drawing

2 Health risk, cancer, mental, stress, patients, treatment, surgery, disease, drugs, doctor

3 Beauty Care beauty, natural, dry, skin, massage, plastic, spray, facial, treatments, soap

4 Higher Education

students, research, board, student, college, education, library, schools, teaching, teachers

5 Software Engineering

service, data, system, services, access, security, development, software, testing, standard

Most predictive Topics given by ARD ranking

Predicting the occupational class: Topics

Rank Label Topic

7 Football van, foster, cole, winger, terry, reckons, youngster, rooney, fielding, kenny

8 Corporate patent, industry, reports, global, survey, leading, firm, 2015, innovation, financial

9 Cooking recipe, meat, salad, egg, soup, sauce, beef, served, pork, rice

12 Elongated Words

wait, till, til, yay, ahhh, hoo, woo, woot, whoop, woohoo

16 Politics human, culture, justice, religion, democracy, religious, humanity, tradition, ancient, racism

Most predictive Topics given by ARD ranking

Predicting the occupational class: Feature analysis

0.001 0.01 0.050

0.2

0.4

0.6

0.8

1

Topic proportion

Use

r pro

babi

lity

Higher Education (#21)

C1C2C3C4C5C6C7C8C9

Topic more prevalent in a class C1-C9 —> CDF line closer to the bottom-right corner


0.001 0.01 0.050

0.2

0.4

0.6

0.8

1

Topic proportion

Use

r pro

babi

lity

Arts (#116)

C1C2C3C4C5C6C7C8C9



0.001 0.01 0.050

0.2

0.4

0.6

0.8

1

Topic proportion

Use

r pro

babi

lity

Elongated Words (#164)

C1C2C3C4C5C6C7C8C9



Jensen-Shannon Divergence between topic distributions across classes

Predicting the income

Preotiuc-Pietro et al. (2015, PloS ONE)


Users Feature vectors GPs £

• 5,191 Twitter users mapped to an average income in GBP (£)

• ~10M tweets


10k 30k 50k 100k0

200

400

600

800

1000

Yearly income (£)

No.

Use

rs

Predicting the income: Features

• Profile (8): #followers, #followees, times listed etc.

• Shallow textual features (10): proportion of hashtags, @-replies etc.

• Inferred psycho-demographic features (15): gender, age, education level, religion etc.

• Emotions (9): joy, anger, fear, disgust etc.

• Word Clusters - Topics (200): Word Embeddings —> Similarity matrix —> Spectral Clustering

Predicting the income: Performance

MAE

9000

9750

10500

11250

12000

Feature Type

£9,535£9,621

£11,456

£10,980

£10,110

£11,291

Profile Demo Emotion Shallow TopicsAll Features

Mean Absolute Error (MAE) of income inference

Predicting the income: Performance

0.21

0.28

0.22

0.27

0.20

0.50 0.51

0.33

0.26

0.32 0.36

0.26

0.61 0.61

0.37 0.36 0.33

0.37 0.36

0.61 0.63

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Profile Psycho-Demo Personality Emotions Shallow Topics All Features

LR SVM-RBF GP

Correlation between actual and predicted income

Predicting the Income: Feature analysis

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)

280003500042000

280003500042000

280003500042000

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 0.15Feature value

Inco

me

Linear vs GP fit

Relation of income and emotion


Linear vs GP fit

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 0.12Feature value

Inco

me

Relation of income and topics


Linear vs GP fit

u1: No.followers (l=47.76) u2: No.friends (l=84.48) u3: No.listings (l=2.65) u4: Foll/fr.ratio (l=5.16)

u5: No.favs (l=96.41) u6: Tw/day (40.96) u7: No.tweets (l=15.94) u8:English Tw. (l=3.12)

28000

36000

44000

28000

36000

44000

0 2000 4000 0 500 1000 1500 2000 2500 0 50 100 150 2 4 6

0 1000 2000 3000 0 5 10 15 20 0 10000 20000 30000 0.25 0.50 0.75 1.00Feature value

Inco

me

Relation of income and profile


Relation of income and psycho-demographic features

●

●

●

●

●

●

●

●

●

●

●

●

●

●30023

36408

30670

32804

34949

32154

24944

32621

27792

35028

31880

34627

32029

32985

Income: Above AverageIncome: Below Average

Religion: UnaffiliatedReligion: Christian

Gender: MaleGender: Female

Ethnicity: CaucassianEthnicity: African American

Education: DegreeEducation: High School

Age: > 35Age: 30−35Age: 25−30

Age: < 25

20000 25000 30000 35000 40000Mean group income (95% CI)


Lampos et al. (2016, ECIR)


Users Feature vectors GPs

upper medium

lower

• 1,342 Twitter users mapped to a socioeconomic class label

• ~2M tweets

upper medium

+ lower

3-way

2-way

Predicting the socioeconomic class: Features

• User Profile (4)

• User bio (523)

• Text in tweets (560)

• Topics - Word Clusters (200)

• User Impact on the platform (4)

• Total of 1,291 features

Predicting the socioeconomic class: Performance

Classification Accuracy (%) Precision (%) Recall (%) F1

2-way 82.05 (2.4) 82.2 (2.4) 81.97 (2.6) .821 (.03)

3-way 75.09 (3.3) 72.04 (4.4) 70.76 (5.7) .714 (.05)

Classification performance (10-fold CV)

Predicting the socioeconomic class: Performance

T1 T2 P

O1 584 115 83.5%

O2 126 517 80.4%

R 82.3% 81.8% 82.0%

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%

Confusion matrices (aggregate)

O = output (inferred), T = target, P = precision, R = recall{1, 2, 3} = {upper, middle, lower} socioeconomic status

Conclusions• User-generated content is extremely useful resource

• infer user demographics

• social science research

• commercial tasks

• User socio-economic status influences language use in social media

• Non-linear models (Gaussian Processes)

• better modelling of demographic inference tasks

• interpretability

• Topic features provide better representations and performance

• Qualitative analysis

• Insights to interesting patterns

AcknowledgementsDaniel Preotiuc-Pietro……………………………………..UPenn

Vasileios Lampos……………………………………………..UCL

Ingemar J. Cox…………………….UCL & Uni. of Copenhagen

Jens K. Geyti………………………………………………….UCL

Bin Zou……..………………………………………………….UCL

Svitlana Volkova……………………………………………..PNNL

Yoram Bachrach…………………………….Microsoft Research

PublicationsD. Preoţiuc-Pietro, V. Lampos and N. Aletras (2015). An Analysis of the User Occupational Class through Twitter Content. In ACL.

D. Preoţiuc-Pietro, S. Volkova, V. Lampos, Y. Bachrach, N. Aletras (2015). Studying User Income through Language, Behaviour and Affect in Social Media. PLOS ONE.

V. Lampos, N. Aletras, J. K. Geyti, B. Zou, I. J. Cox (2016). Inferring the Socioeconomic Status of Social Media Users based on Behaviour and Language. In ECIR.

Thank you Questions?