AINL 2016: Khudobakhshov
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This document discusses methods for estimating a user's actual age and gender when those values are not directly provided. It outlines using social graph analysis, natural language processing, analyzing user interests, and statistical methods. For social graph analysis, it examines using connections like classmates to infer age and analyzing local graph properties. NLP looks at gender-specific language in user profiles while interest analysis matches users to gender-biased communities. Statistics applies overall patterns in the data to make estimations.
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AINL 2016: Nikolenko
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AINL 2016: Kuznetsova
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AINL 2016: Moskvichev
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LEVELLINGTHEPLAYINGFIELDWhy single stude.docx
LEVELLING
THE
PLAYINGFIELD
Why single student mothers are finding it harder to go back and stay in school
1
Introduction
Problem Statement
User Research
Personas
Competitive Analysis
Next Steps
“A good mom wants her child’s outcome
to be better than her own.”
Shannon Bradley
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
3
The world isn’t perfect, some people have more than others...
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
4
But… this is 2016! Everyone is equal and has the same opportunities…
… Don’t they?
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
5
But… this is 2016! Everyone is equal and has the same opportunities…
… Don’t they?
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
6
“We live in a system that espouses merit, equality, and a level playing field, but exalts those with wealth, power, and celebrity, however gained.”
― Derrick A. Bell
Ethical Ambition: Living a Life of Meaning and Worth
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
7
So what can we do??
We can
LEVEL
the playing field!
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
8
But WHO do we help?
Did you know....?
4.8 Million College Students are Raising Children[1]
2,049,242 are single student mothers
Of the 2,582,340 single parents, only 533,098 are single fathers
[1] Barbara Gault, Lindsey Reichlin, Elizabeth Reynolds, and Meghan Froehner. 2014. 4.8 Million College Students are Raising Children. 4.8 Million College Students are Raising Children. Retrieved November 6, 2016 from http://www.iwpr.org/publications/pubs/4.8-million-college-students-are-raising-children/at_download/file
Next Steps
Problem Statement
Introduction
User Research
Personas
Competitive
Analysis
9
So what’s the
PROBLEM?
?????????
?????????
Next Steps
Introduction
User Research
Personas
Competitive
Analysis
Problem Statement
[1] Min Zhan and Shanta Pandey. 2004. Postsecondary Education and Economic Well-Being of Single Mothers and Single Fathers. Journal of Marriage and Family 3, 66, (July 2004), 661-673. DOI: http://dx.doi.org/10.1111/j.0022-2445.2004.00045.x
[2] Sheila Katz. 2013. "Give Us a Chance to Get an Education": Single Mothers' Survival Narratives and Strategies for Pursuing Higher Education on Welfare. Journal of Poverty 3, 17, (July 2013), 273-304. DOI: http://dx.doi.org/10.1080/10875549.2013.804477
As WHILE they are their children, they must
When compared to single fathers, single mothers have a harder time trying to raise their kids.
RAISING
REGULARLY ATTEND SCHOOL AS WELL[1].
Next Steps
Introduction
User Research
Personas
Competitive
Analysis
Problem Statement
11
[1] Min Zhan and Shanta Pandey. 2004. Postsec ...
COMM 308 Project
The document compares the emotional responses of men and women to social media use. It begins with an introduction and literature review on previous research findings about gender differences in social media usage and emotional responses. It then outlines the study's qualitative and quantitative methods. For the qualitative portion, interviews were conducted with 4 men and 4 women to identify themes in emotional responses. For the quantitative portion, a survey was administered to further examine relationships between gender and positive, negative, and neutral emotional responses to social media. Statistical analysis of the survey data found some differences in how men and women view the benefits and enjoyment of Facebook use.
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AINL 2016: Khudobakhshov
- 1. Развлекательная социальная сеть Одноклассники 2016 How to Estimate User’s Actual Age and Gender Vitaly Khudobakhshov
- 2. Agenda • Problem statement • Social graph analysis • NLP methods • Behavior and user’s interests analysis • Statistical approach Vitaly Khudobakhshov, 2016
- 3. 1 Problem statement Vitaly Khudobakhshov, 2016 It is not about a situation where user consciously hides his or her gender or age and behaves consistently.
- 4. 2 Problem statement Vitaly Khudobakhshov, 2016 • Let’s suppose that we have users who don’t set their birth date or gender (default value problem) • or set wrong values for some reason (e.g. mistakes and so on)
- 5. 3 Problem decomposition Vitaly Khudobakhshov, 2016 Age Estimation Social Graph Analysis Gender Estimation NLP Interests Statistics
- 6. 4 Social Graph Analysis Social Graph • Is represented as an adjacency list • user -> [(user0, label0), (user1, label1),…] • Social graph is an undirected graph with labeled edges • An edge may have multiple labels (classmates, parents, etc.) Vitaly Khudobakhshov, 2016
- 7. 5 User’s Graph What is a User’s Graph? • User’s graph is a graph which is induced by star- shaped tree • user -> [(user0, label0), (user1, label1),…] Vitaly Khudobakhshov, 2016 John John’s Mother John’s Father John’s Girlfriend AaronDavid Sara
- 8. 6 Social Graph Analysis Local Properties of User’s Graph • Number of friends • Connected components • Number of triangles • and so on Vitaly Khudobakhshov, 2016
- 9. 7 Age Estimation by Local Properties Motivation Vitaly Khudobakhshov, 2016 John 1995 1970 1992 ? 1992 1968 Classmates Parents Relationship
- 10. 8 Age Estimation by Local Properties Data Sources • Classmate label should be a strong feature (school, college). • Colleague label definitely is not that good. • How about a group of friends who are the same age? Vitaly Khudobakhshov, 2016
- 11. 9 Some obstacles Quality of the Model • No ground truth. • How to check? Vitaly Khudobakhshov, 2016 Quality of the Data • Labeling is incomplete.
- 12. 10 Age Estimation: Step 1 Vitaly Khudobakhshov, 2016
- 13. 11 Confidence Vitaly Khudobakhshov, 2016 Which source of the estimation is better? The first attempt is something like this: C = 1 – 1 / #friends Does it work?
- 14. 12 Age Estimation: Step 2 Vitaly Khudobakhshov, 2016 1 – classmates (school) 2 – classmates (college) 3 – max component Not so good
- 15. 13 Confidence Vitaly Khudobakhshov, 2016 Common sense formula Here is an easy way to solve the problem: Cschool = 1 – 1 / #friends + 0.002 Ccollege = 1 – 1 / #friends + 0.001 Cmax = 1 – 1 / #friends
- 16. 14 So you want to write a fugue? Model quality • No ground truth. • There are special cases (e.g. Eschool=Ecollege=Emax). • We can try to maximize accuracy with respect to model parameters. Vitaly Khudobakhshov, 2016
- 17. 15 NLP and Gender Estimation Advantages Vitaly Khudobakhshov, 2016 • Simple models are easy to understand: I/YOU + ADJ/VERB with gender Disadvantages • Very difficult in case of a multilingual environment • Coverage is not very good • Privacy concerns
- 18. 15 Communities and Interests How it works Vitaly Khudobakhshov, 2016 • Male persons prefer cars and extreme sports. • Female persons prefer something else. Conclusion • There are gender specific communities and gender neutral communities. • Divide and rule
- 19. 16 Interests and Gender Estimation Vitaly Khudobakhshov, 2016
- 20. 17 Interests and Gender Estimation Advantages Vitaly Khudobakhshov, 2016 • Language independent • Good coverage Disadvantages • Thresholds selection • Small and gender neutral communities
- 22. 17 Statistics Advantages Vitaly Khudobakhshov, 2016 • Language independent • Not very sensitive to special characters (or may be preprocessed) • Near to maximum possible coverage
- 23. 18 Conclusion Vitaly Khudobakhshov, 2016 • Models are complimentary to each other. • Simple methods may produce very good results due to big data issues. • We can gain better results without privacy violation.