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Big Data and the Global Gender Gap: The Promises and Perils of Digital Information
Rebecca Furst-NicholsDeputy Director, Data2X
Bapu VaitlaFellow, Data2X
UN System Staff College Knowledge Centre for Sustainable DevelopmentSD Talks Special Series on Data for Sustainable DevelopmentMarch 20th, 2018
Data2X: Our Mission
Data2X works to increase the availability and use of quality gender data. We:
• Promote expanded, unbiased, and innovative gender data collection.• Identify gender data gaps for priority attention.• Lead partnerships to close gender data gaps.• Advocate for better gender data and its use in decision making.• Educate about how to improve and use gender data to improve lives and
outcomes for all.
What is Gender Data?
• Data that is disaggregated by sex, such as primary school enrollment rates for girls and boys
• Data that pertains specifically to girls and women as a result of biology or their social roles, such as maternal mortality rates or unpaid care work
Where are the Gender Data gaps?
There are over 28 identified gaps in gender data based on need, population coverage, and policy relevance across five domains:
Defining Big Data
Partnerships: Big Data for Gender
Geospatial Data (Satellite Imagery)Flowminder Foundation
1. Obtain survey point data on well-being (e.g., DHS)
2. Obtain geospatial data at same locations: population density, infrastructure, vegetation type (satellite, etc.)
3. Correlate the two sets of info
4. Predict landscape of data on well-being
Geospatial Data (Satellite Imagery)Flowminder Foundation
1. Obtain survey point data on well-being (e.g., DHS)
2. Obtain geospatial data at same locations: population density, infrastructure, vegetation type (satellite, etc.)
3. Correlate the two sets of info
4. Predict landscape of data on well-being
Credit Card/Cell Phone Data: Economic LifestylesDi Clemente, Gonzalez, et al. (MIT)
- Anonymized credit card data from 150k users, with age, sex, location info
- Subset: cell phone data- Portraits of economic lifestyles:
mobility, access, preferences- Could illuminate how women
cope with economic/environmental shocks & stresses
Credit Card/Cell Phone Data: Economic LifestylesDi Clemente, Gonzalez, et al. (MIT)
- Anonymized credit card data from 150k users, with age, sex, location info
- Subset: cell phone data- Portraits of economic lifestyles:
mobility, access, preferences- Could illuminate how women
cope with economic/environmental shocks & stresses
Cell Phone Data: EpidemiologyWesolowski et al. (Carnegie Mellon, Harvard, etc.)
- Locations of ~15m cell phone subscribers; travel maps
- Malaria prevalence map based on existing data
- Source/sink maps based on human movement and parasite prevalence
- Allows precisely targeted interventions, in time and space
Cell Phone Data: EpidemiologyWesolowski et al. (Carnegie Mellon, Harvard, etc.)
- Locations of ~15m cell phone subscribers; travel maps
- Malaria prevalence map based on existing data
- Source/sink maps based on human movement and parasite prevalence
- Allows precisely targeted interventions, in time and space
Cell Phone Data: EpidemiologyWesolowski et al. (Carnegie Mellon, Harvard, etc.)
- Locations of ~15m cell phone subscribers; travel maps
- Malaria prevalence map based on existing data
- Source/sink maps based on human movement and parasite prevalence
- Allows precisely targeted interventions, in time and space
Cell Phone Data: EpidemiologyWesolowski et al. (Carnegie Mellon, Harvard, etc.)
- Locations of ~15m cell phone subscribers; travel maps
- Malaria prevalence map based on existing data
- Source/sink maps based on human movement and parasite prevalence
- Allows precisely targeted interventions, in time and space
Social Media: Patterns of Mental Health on TwitterDe Choudhury et al. (Georgia Tech)
1. Key signal phrases (validated with diagnostic test)
2. Analyze positive/negative affect, cognitive attributes, lexical density, social concerns, etc.
3. Compare men/women, control versus mental illness disclosure samples
Social Media: Patterns of Mental Health on TwitterDe Choudhury et al. (Georgia Tech)
1. Key signal phrases (validated with diagnostic test)
2. Analyze positive/negative affect, cognitive attributes, lexical density, social concerns, etc.
3. Compare men/women, control versus mental illness disclosure samples
IV. Internet Activity 26
We can further break down the attribute
categories. Female users in the MID sample
show 15.4% higher sadness and 10.7%
higher anxiety; prior literature indicates
that expression of these emotions is asso-
ciated with depression, mental instability,
and feelings of helplessness, loneliness, and
restlessness. However, female users also tend
to use 7.1% more positive af ect in their
content, perhaps to demonstrate a positive
outlook publicly despite the mental health
challenges they are facing. Male users, on
the other hand, express 2.6% more negative af ect overall, including 5.3% higher anger
and 9.5% more expressions with swearing. Females express fewer cognitive attributes on
social media than do males. Lower usage of words that denote certainty, for example,
may demonstrate heightened emotional instability. T ese dif erences in cognitive expres-
sion are not pronounced in the control
sample, however, suggesting that experience
of mental illness, not intrinsic dif erences
between the sexes, is responsible for the
observed gap.
We turn now to social/personal concerns
and interpersonal focus, both subtypes
within the linguistic style category. Male
MID users display an 8.1% lower sense
of achievement than women and girls,
a known signal of reduced self-esteem.22 Female MID users, meanwhile, express 6.0%
greater concern about their health and 2.7% greater concern about their body, which
may indicate a greater self-awareness about their health or, alternatively, more f xation
with social perceptions about their appearance. Another interesting
f nding is that male MID users exhibit lower use of words having to
do with social concerns, friends, or family. T eir female peers may be
using such language more frequently in their Twitter posts to explicitly
seek help from their social networks. T e interpersonal focus metrics
Figure 16. Differences in linguistic measures between female and male users, disaggregated by mental illness disclosure (MID) and control sample (CTL). Positive values indicate higher scores for female users.
“ #depression has invaded my peace and
#anxiety has exhausted my thoughts. Pain
isn’t always physical
– female user
“ why am I even here... No one needs or wants
me... I’m useless
– female user
Abso
lute
dif
fere
nce b
etw
een
fem
ale
an
d m
ale
use
rs (
%)
Aff
ective
att
rib
ute
s
Cogn
itiv
e
att
rib
ute
s
Lexic
al
den
sity
an
d a
ware
ness
Tem
po
ral
refe
ren
ces
So
cia
l/p
ers
on
al
con
cern
s
Inte
rpers
on
al
focu
s
9
8
7
6
5
4
3
2
1
0
CTL usersMID users
“ Over the past 2 years I have been hit with
physical and mental pain. The pain is real. It
is still ther e.
– female user
• Privacy
• Bias and access: Who does big data leave behind?
• Consider access, affordability, literacy, and other barriers
• Country context: One size doesn’t fit all
• Ground truth
• Digital data should enhance, not replace, information gathered from traditional sources like household surveys and censuses
Big Data: Risks and Considerations
What’s Next? Big Data for Gender Challenge
10 projects representing 29 researchers from 20 global institutions across 8 countries
Women in the Gig Economy: A Data Gap with Implications for Informal Work, Time Use, and PovertyLeads: Overseas Development Institute, Ulula, Data-Pop AllianceMethod: Mobile phone-based longitudinal survey
Gender and Urban Mobility: Addressing Unequal Access to Urban Transportation for Women and GirlsLeads: The GovLab, UNICEF, ISI Foundation, Universidad del Desarrollo, Telefónica, DigitalGlobeMethods: High-resolution satellite data; call detail records
Safety First: Perceived Risk of Street Harassment and Educational Choices of WomenLead: Girija Borker, PhD, Brown UniversityMethods: Student surveys, Google Maps travel route data, mobile application data
Learn more about Data2X at
www.data2x.org/big-data-challenge-awards/