Modeling Human Values with Social Media

Modeling Human
Values with
Social Media
Kyriaki Kalimeri, Yelena Mejova
IC2S2 - July 17, 2019
Amsterdam, The Netherlands
#ValuesWorkshop

Kyriaki Kalimeri
Ph.D. Brain and Cognitive Sciences
@KyriakiKalimeri
Yelena Mejova
Ph.D. Computer Science
@YelenaMejova
Take photos, tweet, link!
@IC2S2
#ValuesWorkshop

“Values” are
broad
motivational
goals!

Care/Harm: virtues of caring and compassion.
Fairness/Cheating: unfair treatment, inequality, notions of justice.
Loyalty/Betrayal: obligations of group membership, loyalty, vigilance against betrayal.
Authority/Subversion: social order, obligations of hierarchical relationships such as obedience,
respect
Purity/Degradation: physical and spiritual contagion,
virtues of chastity, wholesomeness and control of desires.
Liberty/Oppression: feelings of reactance and resentment people feel toward those who
dominate them and restrict their liberty

http://www.worldvaluessurvey.org/WVSContents.jsp

Personality & Values
Values could be considered a higher level
with respect to Personality Traits.
Moral values determine how and when
dispositions and attitudes towards
interpersonal and intergroup processes
relate with our life stories and narratives.
Personality alone does not suffice to
explain our judgments.
McAdams, D., & Pals, J. (2006). A new big five: Fundamental principles
for an integrative science of personality. American Psychologist , 61 ,
204

Motivation
A qualitative and quantitative point of view, allowing us to better
model and understand behaviours, actions, and attitudes towards
social phenomena.

Why modeling human values?
Designing Personalised Communication Campaigns
Policy-making
Self-disclosure & Privacy Considerations
Bridging Societal Polarization
Evolution of Values
Machine Ethics

Attitudes Towards Climate Change

CONTROL FRAMING
Many people are concerned about the health of the natural environment. We are
interested in what you think and feel about this issue.

INDIVIDUALISTIC FRAMING
Many people around the world are concerned about the health of the natural
environment. We are interested in what you think and feel about this issue. Show your
love for all of humanity and the world in which we live by helping to care for our
vulnerable natural environment. Help to reduce the harm done to the environment by
taking action. By caring for the natural world you are helping to ensure that everyone
around the world gets to enjoy fair access to a sustainable environment. Do the right
thing by preventing the suffering of all life-forms and making sure that no one is denied
their right to a healthy planet. SHOW YOUR COMPASSION.

SOCIAL BINDING FRAMING
Many patriotic citizens of the United States are concerned about the health of the natural
environment. We are interested in what you think and feel about this issue. Show you
love your country by joining the fight to protect the purity of America's natural
environment. Take pride in the American tradition of performing one's civic duty by
taking responsibility for yourself and the land you call home. By taking a tougher stance
on protecting the natural environment, you will be honoring all of Creation. Demonstrate
your respect by following the examples of your religious and political leaders who defend
America's natural environment. SHOW YOUR PATRIOTISM!

● Political liberals were consistent in their pro-environmental attitudes across
conditions.
● Political conservatives displayed more pro-environmental attitudes after a binding
moral frame.
Attitudes Towards Climate Change

Attitudes Towards Charitable Giving

Attitudes Towards Charitable Giving
Perceived donation motivation is stronger for care
and loyalty framing
Moral Framing had NO effects neither on
hypothetical not real donations.

Polarized attitudes towards the
poor seem unlikely to be
predicted by political aﬃliation,
while moral values are more
explanatory.
Attitudes towards the Poor

Privacy, Self-Disclosure & Trust

Individuals differ in the degree to which they desire and value personal control over
information about themselves. (Berscheid, 1977)
There is a negative relationship between the value people place on privacy and
perceptions of control over personal information, which is the very foundation of trust
(Stone, 1983)

"But privacy is gone. We gave it up, for no other
reason but the thought that it's useless.
Why don't we protect our rights?"

Attitudes towards Vaccination
Recent interventions have focused on
vaccine knowledge and education and
message framing as methods to
change vaccination attitudes.
➔ Short-term success, but some backﬁre and worsen parental hesitancy.
➔ Temporarily increase vaccination rates but may lead to long-term
dissatisfaction and decreased intention to vaccinate.

Endorsement of harm and fairness -
ideas often emphasized in traditional
vaccine-focused messages - are not
predictive of vaccine hesitancy.
Signiﬁcant associations of purity and
liberty with hesitancy.

Antivax community:
trusts less the conventional authority
sources (presidency, government, EU)
defends more the traditional religious and
moral values and believe newer lifestyles
contribute to the decline of the society. Total population 1232 participants

AV pages are emphasising the value of
“liberty”
PV pages are emphasising the value of
“family”
AV Pages PV Pages

Are Values Changing over Time?
Santos et al. Global Increases in Individualism.
Psychological Science, 2017
Individualism vs. Collectivism reﬂects the extent to
which social relationships are loose, as
opposed to relationships integrated in strong and
cohesive groups.
Individualism increased 12% worldwide since the
‘60s.
Socioeconomic development had the strongest
eﬀect, explaining between 35% and 58% of the
change in individualism over time.

PEW RESEARCH CENTER
Favoring Gay Marriage

Moral Artiﬁcial Intelligence
40 million decisions, in 233 countries
cross-cultural ethical variation

Moral Artiﬁcial Intelligence

Evolution of Surveying Methods
Online & Crowdsourcing platforms (such as AMT) have been extensively validated
as surveying tools and found to be as trustworthy as the traditional surveying
methods.

How many hours do you spend on social networks?

Social Media for Surveying
Recruitment of participants is:
★ Faster
★ Scalable
★ Targeted/Customised cohort
★ Relatively limited cost
★ Eﬃcient when monitoring
fast-evolving phenomena such as
crisis-response or health
emergencies.

Successful Examples on Facebook
Reached 4 million users & the data were
used for personalization, privacy,
persuasion studies
Likeyouth, 60 thousand users in Italy,
Employed for digital humanity studies such as
youth unemployment & vaccination attitudes

The Little Prince
Antoine De Saint-exupéry

Social Media for Research
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. Social
data: Biases, methodological pitfalls, and ethical boundaries. 2016

Social Media as a Surveying Tool
~4,000 participants
Population, Self-selection, and Behavioural biases
Outcome: FB is a valid research tool for administering
demographic and psychometric surveys
BUT do not ignore the intrinsic platform biases.
Kyriaki Kalimeri, Mariano G. Beiro, Andrea Bonanomi, Alessandro Rosina & Ciro Cattuto. Evaluation of Biases in
Self-reported Demographic and Psychometric Information: Traditional versus Facebook-based Surveys

Do not ignore the intrinsic platform biases!
No data source is perfect, and every dataset is imperfect
in its own ways.
Social Media as a Surveying Tool
Zeynep Tufekci. Big questions for social media big data:
Representativeness, validity and other methodological
pitfalls. ICWSM, 14:505–514, 2014.

Social Media Data as a Reﬂection of Values

Real-time
Geolocated
Rich
Multimodal
Spontaneous behaviors
Social & Individualistic

Mobile Apps & Web Searches are greatly informative of
demographics
Moral & Human Values are much harder to predict, STILL,
we get cool insights!

Moral Values
- Care
Schwartz Values
- Conservation
- Tradition
+ Openness
+ Self-Enhancement
Demographics
Low income
Low wealth
Not a Parent
Single
18 to 24 years old
Not a smoker

Michael F. Schober, Josh Pasek, Lauren Guggenheim, Cliﬀ Lampe, and
Frederick G. Conrad. Social media analyses for social measurement. Public
Opinion Quarterly, 80(1):180–211, 2016

Social media as reﬂection of values
Michael F. Schober, Josh Pasek,
Lauren Guggenheim, Cliﬀ
Lampe, and Frederick G.
Conrad. Social media analyses
for social measurement. Public
Opinion Quarterly,
80(1):180–211, 2016.

Schober et al. 2016
Initiative
Informed consent
Ability to opt out
Prior consideration
Identity of user
Perceived audience
Size of perceived audience
Social desirability pressure
Potential for manipulation
Time pressure/synchrony
Burden

Schober et al. 2016
Initiative
Informed consent
Ability to opt out
Prior consideration
Identity of user
Perceived audience
Burden
xkcd 1390

PNAS 2014
https://www.theguardian.com/technology/2014/ju
n/29/facebook-users-emotions-news-feeds

Schober et al. 2016
Initiative
Informed consent
Ability to opt out
Prior consideration
Identity of user
Perceived audience
Burden
Sarah Andersen

Schober et al. 2016
Initiative
Informed consent
Ability to opt out
Prior consideration
Identity of user
Perceived audience
Burden
xkcd 632

"Botornot: A system to evaluate social bots."
Davis, Clayton Allen, et al. WWW, 2016.
https://botometer.iuni.iu.edu
Ratkiewicz et al WWW, 2011
Truthy project

"Botornot: A system to evaluate social bots."
Davis, Clayton Allen, et al. WWW, 2016.
Ratkiewicz et al WWW, 2011
Truthy project
https://www.computerworld.com/article/2843572/computer-scientists-s
ay-meme-research-doesnt-threaten-free-speech.html

Schober et al. 2016

Schober et al. 2016
Temporal properties
Population coverage
Topic coverage
Sampled units
Sampling frame
Sampling procedure
Sample size
Relevance to research topic
Granularity of analyses
Data structure
Automatically generated
auxiliary information

Schober et al. 2016
Temporal properties
Population coverage
Topic coverage
Sampled units
Sampling frame
Sampling procedure
Sample size
Data structure
xkcd 723

Can we predict which of Reddit users in Mental
Health will later post in Suicide Watch?

Schober et al. 2016
Temporal properties
Population coverage
Topic coverage
Sampled units
Sampling frame
Sampling procedure
Sample size
Data structure
https://www.smbc-comics.com/comic/2014-11-14

Schober et al. 2016
Cost to researchers
Research communities
Ethics of consent for use of
data
Ethics review of research
protocol
Analytic approach
Potential for research bias
Evaluating model quality
Adjustments for
non-representativeness
Stability of data source
Ownership of data
Perception of research
enterprise

Schober et al. 2016
Cost to researchers
data
protocol
Analytic approach
Adjustments for
Ownership of data
enterprise
xkcd 749

Schober et al. 2016
Cost to researchers
data
protocol
Analytic approach
Adjustments for
Ownership of data
enterprise
xkcd 1838

Schober et al. 2016
Cost to researchers
data
protocol
Analytic approach
Adjustments for
Ownership of data
enterprise
xkcd 221

Schober et al. 2016
Cost to researchers
data
protocol
Analytic approach
Adjustments for
Ownership of data
enterprise
xkcd 1998

Politics
Nutrition
Religion

Politics
Is the moral rhetoric on Twitter related to violence during protests?
2015 Baltimore protests
A moral tweet: one that is related to one of the moral foundations in Moral
Foundations Theory (annotator agreement Kappa = 0.636 in moral/non-moral)
Train LSTM neural network on 4,800 training tweets, achieve 89.01% accuracy
Moralization in social networks and the
emergence of violence during protests
Mooijman, Hoover, Lin, Ji, Dehghani. Nature Human Behavior 2018

Politics
“In terms of incidence ratios, [...] the count of moral tweets on days with violent protests is 1.88 times
that of days with no protests, holding the other variables in the model constant. No such association
was observed for peaceful protest days. ”

Politics
“An independent variable is said to ‘Granger cause’ a dependent variable when previous values of
the independent variable predict future values of the dependent variable above and beyond
predictions based on past values of the dependent variable alone”

Politics
“These analyses indicate a bidirectional Granger causal relationship, such that the count of moral
tweets predicts the future count of arrests and the count of arrests predicts the future count of
moral tweets.”

Politics
2 Survey studies, Example: Charlottesville, VA clashes in August 2017
Study 1: moralization vs. acceptability of using violence
“To what extent protesting the far-right is a moral issue?
“Violence against far-right is justifiable if fewer people join it”
Confirmed, even after controlling for political orientation
Study 2: moral convergence & moralization vs. acceptability of using violence
Told “the majority of (versus few) people in the United States share your particular moral values”
Observed “a significant interaction effect between moralization and moral convergence”, while
“moral convergence was (overall) unrelated to the acceptability of violence”.

Politics
“moralization of a protest can
increase the acceptability of using
violence at this protest. In
addition, this only occurred when
participants perceived to share
their moralized attitudes with
others and increased their
attitude certainty.“

Politics
“Islamic State in Iraq and the Levant” vs “ISIS”
#FailedRevolutions: Using Twitter to study the
antecedents of ISIS support
Magdy, Darwish, Weber. First Monday 21(2), 2016

Politics
1. full name of the group
“‫ﻲﻣﻼﺳﻹا‬ ‫”ﺔﻟودﻟا‬ (Aldawla Alislamiya — “Islamic State”) or “‫ﻲﻓ‬ ‫قارﻌﻟا‬
‫مﺎﺷﻟاو‬ ‫”ﺔﯾﻣﻼﺳﻹا‬ (Aldawla Alislamiya ﬁ Aliraq walsham — “Islamic
State in Iraq and the Levant”)
2. abbreviated version of the name
“‫”عاد‬ (da’esh — Arabic acronym for the group), “‫”شﻋ‬ (da’eshy —
from da’esh), or “‫”عاود‬ (dawa’esh — plural of da’eshy)
3.9 million tweets
13 Oct - 31 Dec 2014
#FailedRevolutions: Using Twitter to study the

Politics #FailedRevolutions: Using Twitter to study the
1. full name of the group
“‫ﻲﻣﻼﺳﻹا‬ ‫”ﺔﻟودﻟا‬ (Aldawla Alislamiya — “Islamic State”) or “‫ﻲﻓ‬ ‫قارﻌﻟا‬
‫مﺎﺷﻟاو‬ ‫”ﺔﯾﻣﻼﺳﻹا‬ (Aldawla Alislamiya ﬁ Aliraq walsham — “Islamic
State in Iraq and the Levant”)
2. abbreviated version of the name
“‫”عاد‬ (da’esh — Arabic acronym for the group), “‫”شﻋ‬ (da’eshy —
from da’esh), or “‫”عاود‬ (dawa’esh — plural of da’eshy)
3.9 million tweets
13 Oct - 31 Dec 2014
Select accounts mentioning ISIS at least 10 times
And which use same name form 70% of the time
Manually assessed accuracy 98%

Hashtags predicting ISIS support

Hashtags predicting ISIS opposition

Politics Revisiting the American Voter on Twitter
Le, Boynton, Mejova, Shafiq, Srinivasan. CHI, 2017

Most important factors for
choosing a president are:
● Party / partisanship
● Policy considerations
● Personality of candidate

Nov 15, 2015 - Feb 29, 2016
49,564,856 tweets
11,043,452 users
Twitter Streaming API
Candidate full name as query

Party
User following
“landmark”
accounts with
known
partisanship
Personality
Modiﬁed
Adjective Check
List (ACL) having
14 categories:
moderation,
forcefulness,
pettiness...
Policy
From previous
literature,
keywords for 11
policy categories:
immigration,
economy, gun
control...
Sentiment
SentiWordNet
which is
expansion of
WordNet
dictionary

“negative tweets account for about four-ﬁfths of all personality-related tweets overall, Republicans
tend to be more negative than Democrats (83% vs. 77%). As compared to Republicans,
Democrats are more positive about paciﬁsm and more negative about machiavellianism and wit.”

Clinton is seen as moderate,
but not Trump or Sanders
Sanders is seen as most
friendly and cute
Clinton and Christie are seen
as most machiavellian

Are these important in modeling favorability ratings of politicians?
Linear regression on polls aggregated by RealClearPolitics
Feature selection using AIC, modeling Republicans and Democrats separately

Politics
Track aﬃliation with an idea
Track changes over time
Qualitative data to understand values
Social interaction

Nutrition
● 164K locations
● 20.8M posts
● 3.3M users
● 316 US counties
#FoodPorn: Obesity Patterns in Culinary Interactions
Mejova, Haddadi, Noulas, Weber. DH, 2015

Nutrition
● Users post more photos in non-fast-food restaurants
● Correlation between obesity rate & fast food check-ins is 0.424

Nutrition
● Posts from low-obesity counties get more likes and comments than in high-obesity areas
(socio-economic correlates?)
● More information can be glimpsed from other hashtags associated with visiting restaurants

Nutrition #FoodPorn: Obesity Patterns in Culinary Interactions
Restaurant
categories
with
#foodporn
Likes on
posts from
restaurant
categories

Nutrition Anorexia on Tumblr: A Characterization Study
De Choudhury. DH, 2015
WARNING:
DISTURBING BODY
IMAGERY

Values are inferred from posted and liked content
Surveying these vulnerable populations directly may be diﬃcult
Nutrition Anorexia on Tumblr: A Characterization Study
De Choudhury. DH, 2015
Predicting pro-ana vs pro-recovery users

fitspiration, fitsporation, fitspo
n = 1,050 posts
March 25-26, 2015
Nutrition Skinny is not enough: A content
analysis of fitspiration on Pinterest
Simpson & Mazzeo. Health Comms, 2017

Nutrition Skinny is not enough: A content
analysis of fitspiration on Pinterest
Simpson & Mazzeo. Health Comms, 2017
Appearance vs Health Focus
Body Image Standards
Outcome Expectations

Nutrition
1,055,196 tweets: obese or obesity
2,889,764 tweets: diabetes or diabetic
July 19, 2017 - Dec 31, 2017
Information Sources and Needs in the
Obesity and Diabetes Twitter Discourse
Mejova. DH, 2018

Nutrition Information Sources and Needs in the
Mejova. DH, 2018
Hand-annotated topics of questions
Prevalent discussion of whether
obesity is a disease and whether
social acceptance of obesity as
disease is desirable
Diabetes has smaller discussion on
social perception of diabetics

Nutrition Information Sources and Needs in the
Mejova. DH, 2018
Crowdsourced annotation
Fat shaming:
27.6% Obesity, 5.9% Diabetes
Personal information:
Personal responsibility:

Nutrition
Values revealed via observed behavior
Values may be unhealthy, merging into mental problems
Values may be promoted by people or other parties
Discussions reveal attitudes

Religion
Halal ‫ﺣﻼﻻ‬
In Islam, Halal is an Arabic term meaning “lawful or permissible” and not only
encompasses food and drink, but all matters of daily life.
Haram
That which is not permissible under Islamic law
#Halal Culture on Instagram
Mejova, Benkhedda, Khairani. Frontiers in Digital Humanities 2017
Source: Islamic Services of America

Halal (English) 1,004,445 posts by 120,943 users
‫ﺣﻼﻻ‬ (Arabic) 325,665 posts by 11,516 users
Data collection in April 2016
Religion #Halal Culture on Instagram

English ArabicIndonesian
Topic extraction using LDA, the 50 topics annotated using Grounded Theory with
primary and secondary codes by proﬁcient language speakers.

Posts mentioning religion get many more likes in Arabic (but everything else does too)
Posts mentioning foods get fewer likes when they also mention regulatory agencies like FDA (in
Indonesian and English)
EnglishArabic Indonesian

Social media may be a window
into the open interpretation and
changing nature of religious
concepts

Religion
Tweet histories of over 12K users
who claim to live in Qatar and
have a common Qatari last name.
Span Sep 2006 - July 2014
Privacy and Social Media Use in the Arabian
Gulf: Saudi Arabian & Qatari Traditional Values
in the Digital World
Abokhodair, Abbar, Vieweg, Mejova. Journal of Web Science, 2017

● No single word for “privacy”
● Manual analysis of popular
keywords, excluding
ambiguous ones
● LDA topical analysis to
exclude non-relevant topics
● Final tweets: 1,772
Religion Privacy and Social Media Use in the Arabian

Mainly associated with fear,
demerit, haram, culture,
society, people. Also:
Facebook and policy
Spaces in which privacy
should apply
Terms associated with Khososyah (privacy)

Anything that should not be
disclosed in public.
Close connection to God
“Do not violate others’ privacy if
you do not want your own privacy
violated”
Accumulation of good deeds
Terms associated with Ird (honor)

Computer
Mediated
Discourse
Analysis

● manually labeled for gender
● user’s name, screen name,
proﬁle
● 47% female
● contributed 1.367 tweets
(men 1.445 tweets)
more female more male

Privacy is gendered
Privacy is what one is exposed to
Privacy is also after death
Privacy is moral in religious sense

pro-Muslim vs anti-Muslim response after
November 13, 2015 Paris attacks
8.36M tweets on Nov 14 - 16
Religion #isisisnotislam or #deportallmuslims?:
Predicting unspoken views
Magdy, Darwish, Abokhodair, Rahimi, Baldwin. WebSci, 2016

Predicting post-even stance using content, proﬁle, and network features and SVM classiﬁer
Religion #isisisnotislam or #deportallmuslims?:
Predicting unspoken views
Magdy, Darwish, Abokhodair, Rahimi, Baldwin. WebSci, 2016

Conservative media outlets
Presidential primaries
Evangelical Christian
preachers
Political and foreign issues
pro-Israel media
Atheists
Secular Muslims
anti-Islam content
Abortion
Liberal media outlets
Presidential primaries
US President (Obama)
Social issues like abortion,
race relations, same sex
marriage, gun control
Foreign media outlets
Muslim academics
Support for Muslims
African American media
Negative
Positive

Religion
Track the way religion aﬀects everyday behaviors & beliefs
Track opinions about religion
Location-speciﬁc culture
Development over time

Does Social Media change our values?

Discussion
Data ownership
Data / model bias
Informed / meaningful consent
Ethical intervention design

Discussion - bias
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. Social data: Biases, methodological pitfalls, and
ethical boundaries. 2016.

Discussion - bias
Zeynep Tufekci. Big questions for social media big data: Representativeness, validity and other methodological
pitfalls. ICWSM, 14:505–514, 2014.
Twitter becoming a model
organism because of accessible
API and public data.
But is it representative of
interactions on internet?

Discussion - meaningful consent
● Disclosure: provide participant with accurate information about beneﬁts
and harms
● Comprehension: participant must understand what is being disclosed
● Voluntariness: participant can reasonably resist participation
● Competence: participant has mental, emotional and physical
competences to give informed consent
● Agreement: participant must have clear opportunity to accept or decline
● Minimal Distraction: participant’s attention should not be diverted
Friedman, B., Lin, P., & Miller, J. K. (2005). Informed consent by design. Security and Usability, 503-530.

Discussion - ethical interventions
Who deﬁnes the
desirable values?

Surveys
https://moralfoundations.org
https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=1116&context=orpc
https://likeyouth.org/

Data
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.F. and
Rahwan, I., 2018. The moral machine experiment. Nature, 563(7729), p.59.
https://likeyouth.org/
https://osf.io/k5n7y/
https://github.com/oaraque/moral-foundations

Shalom H Schwartz. 2012. An overview of the Schwartz theory of basic values.Online Readings in Psychology and Culture 2, 1 (2012), 11.
Haidt, J., 2003. The moral emotions. Handbook of affective sciences, 11(2003), pp.852-870.
Jonathan Haidt and Craig Joseph. 2004. Intuitive ethics: How innately prepared intuitions generate culturally variable virtues. Daedalus 133, 4
(2004), 55–66.
Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S.P. and Ditto, P.H., 2013. Moral foundations theory: The pragmatic validity of moral
pluralism. In Advances in experimental social psychology (Vol. 47, pp. 55-130). Academic Press.
Iyer, R., Koleva, S., Graham, J., Ditto, P. and Haidt, J., 2012. Understanding libertarian morality: The psychological dispositions of self-identified
libertarians. PloS one, 7(8), p.e42366.
Feldman, G. Personal values and moral foundations: Examining relations and joint prediction of moral variables.
McAdams, D., & Pals, J. (2006). A new big five: Fundamental principles for an integrative science of personality. American Psychologist , 61 ,
204
Wolsko, C., Ariceaga, H., & Seiden, J. (2016). Red, white, and blue enough to be green: Effects of moral framing on climate change attitudes
and conservation behaviors. Journal of Experimental Social Psychology, 65 , 7 - 19.
http://www.sciencedirect.com/science/article/pii/S0022103116301056. doi:http://dx.doi.org/10.1016/j.jesp.2016.02.005.
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