This document discusses several approaches to promoting individual and societal well-being through the analysis of large datasets. It explores analyzing fitness and stress data to identify different "fitness groups" and their relationship. It also examines social isolation and loneliness, identifying different types of loneliness and how people recover from loneliness. Finally, it proposes analyzing social media data to detect emotions and their dynamics over time to create "citizen mindscapes" and better understand national emotions and well-being.

1. Approaches to Promote
Individual and Societal
Wellbeing
Krista Lagus
krista.lagus@helsinki.fi
Artificial Intelligence and Machine Learning for People
2.11.2015

2. Contents
— Paths of physical and mental wellbeing: from health
to wellbeing & wellness interventions
— Exploring Social isolation and loneliness: Are there
loneliness types, how do people recover?
— Citizen’s Mindscapes: Dynamics of emotions in
social big data

3. Sports Institute of Finland
(Vierumäki) fitness data
>100,000 measurements in 20+ years
small subset with also mental workload & stress evaluation
(Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)
males females
example: abdominals
all
40-50
years
old
What kind of different
”fitness groups” can
be found?
Relationship between
physical & mental
wellbeing (stress)?
Do interventions help?

4. Map of fitness and stress

5. Individual wellbeing paths on
the map of fitness and stress

6. Social isolation & loneliness
— Social isolation is a severe health risk both
physically and mentally
— Even brief ostracism appears to be experienced in the brain
as intense physical pain (Williams, 2011)
— Continuous experience of pain is a continuous stress, leading to
stress-related diseases
— What different types of loneliness is there?
— How do people recover from loneliness?
?

7. Text questions
(in Yksinäisyyskysely 2011, 500 responses)
1. Miten sinusta tuli yksinäinen? How did you become lonely?
2. Miltä yksinäisyys tuntui? Miten se vaikutti mieleesi ja
käyttäytymiseesi? How did it feel? How did it affect your mind
and behaviour?
3. Miten selvisit yksinäisyydestä (tai siitä huolimatta)? How did
you survive loneliness (or despite it)?
4. Tiesivätkö lähipiirisi ihmiset yksinäisyydestäsi? Miten he
suhtautuivat siihen? Did people close to you know about your
loneliness? How did they react?
5. Mitä haluaisit sanoa muille vastaavassa tilanteessa oleville?
What would you like to say to others in a similar situation?

8. Closed questions:
During worst time of your life, did you feel
CONTENTACCEPTEDHAPPY
DEPRESSED SADLONELY
CALM
(high value=red)

9. Closed questions:
How (dis)content are you now with
(discontent=red)
ONESELF FRIENDS
FREE TIME
FAMILY RELATIVES
LIVING STD WORK HEALTH

10. Segmenting loneliness
(discontent=red)
family relationships
support
completely
alone
problems now: freetime, work,
self, health, standard of living,
Problems with
friends Problems with family
Krista Lagus, Juho Saari, Ilari T. Nieminen, and Timo Honkela. Exploration of loneliness
questionnaires using the self-organising map. Proc. ICANN 2013, pp. 405–411, 2013.

11. Positive change in
loneliness - what is happening?
LONELINESS
LAST MONTH
LONELINESS IN WORST
TIME OF LIFE
HAPPINESS
LAST MONTH
Node 64, what helped:
Professional help: psychotherapy/Aslak/perheasiain neuvottelu
Christ / spirituality /religion / god,
found an amazing friend / some positive encounters
running, crying, meeting my own emotions, forgiveness
moving, hobbies, culture

12. Status now
— Network of loneliness researchers led by prof. Juho Saari collected a
27,000 people data set on loneliness (HS questionnaire 2014)
— Tens of questions on various aspects of health, wellbeing and
experienced loneliness
— A text question: 3400 people answered “How does loneliness feel”?
— Some initial experiments done – no funded project for systematically
analyzing the data
— Could we discover “loneliness types”?
— Correlation btw written description of experienced loneliness and
wellbeing indicators?

13. CITIZEN MINDSCAPES
THE CHEMISTRY OF A NATION

14. AFFECTIVE CONTAGION:
DISCOVERING
DYNAMICS OF EMOTIONS

15. Challenges
— How to recognize mass
emotions from texts?
— How to detect the dynamic
change of emotions within
discussion threads?
— Strategies and roles of
discussants? Troll, Diplomat?
— Sentiment analysis is
challenging, typically
positive/negative categories
only obtained with sufficient
accuracy
— PERMA: 5-dimensional
theory of wellbeing &
associated vocabulary
— Ad hoc: Vocabularies of
emotional terms
— Empirical linguistic theory of
emotional expressions: Seija
Tuovila dissertation on
emotions (in Finnish)
Resources
— Lack of knowledge on the
mapping between felt emotions
and textual expressions
— National differences:
Translation approaches may
not be sufficient

16. Background: PERMA analysis of
Big data conversations
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
ENRON Wikipedia EUROPARL
Positivity
Meaning
Achievement
Honkela, Korhonen, Lagus, Saarinen (2014). Five-dimensional sentiment analysis of
corpora, documents and words. Proceedings of WSOM 2014.
EU (EUROPARL) discussions
are full of meaning, low on
talk how to achieve
ENRON corporate emails are
positive and talk about
concrete achievements but
lack talk of meaning
VS

17. Dynamics over time: Joy and
Happiness peaks in 2005 and 2009
Data: about 3 million
comments in suomi24, a
nationally representative
chat forum
Frequency analysis based on
synonym dictionary definitions of
emotions
Searches using Korp.csc.fi

18. Discussion area profiles:
where is most fear & worry?
Health Society
Relationships
Data: about 3 million comments in
suomi24, a nationally
representative chat forum
Frequency analysis based on synonym
dictionary definitions of emotions
Searches using Korp.csc.fi

19. Daily rythms
• Lunchtime Activity Peak: At 11-12
lots of comments & lengthy
comments!
• Fast-paced evening: 21-23 most
new threads & comments, &
shortest comments
• Hour of the Wolf: At 04-05 longest
comments
• Asleep: 05-06
0
1
2
3
4
5
6
7
1 3 5 7 9 11 13 15 17 19 21 23
Number of comments (24h)%
32
33
34
35
36
37
38
39
40
41
42
1 2 3 4 5 6 7 8 9 101112131415161718192021222324 h
Word count per comment (24h)
0
1
2
3
4
5
6
7
1 3 5 7 9 11 13 15 17 19 21 23
Ketjujen
aloituksia
Kommentteja
Sanoja
%
Data: 56 million posts of Suomi24

20. CITIZEN OBSERVATORY vs.
EMPOWERING CITIZENS?
JOY-O-METER DATA: SUOMI24
Lagus, Pantzar, Ruckenstein (2015), “Keskustelun tunneaallot –
Suomi24 -avoimen datan hanke. Tieteessä Tapahtuu, (in press)
Facebook group: Citizen Mindscapes