Reality mining is defined as the quantification and modeling of long-term human behavior and social interactions through mobile and wearable technology. The document explores the motivation, examples, research applications, and challenges related to reality mining, emphasizing its implications for privacy and data analysis. It concludes with the need for collaboration between social and computer scientists to harness the potential of large-scale data while safeguarding individual privacy.

1. Reality
Mining

2. Virgílio Solano
RA 180158
Universidade Estadual de Campinas
Instituto de Computação
Web e Web Semântica
Prof. André Santanchè

3. Agenda
◎Introduction
◎What’s Reality Mining?
◎Motivation
◎Examples
◎Research
◎Challenges
◎Conclusion
◎Questions

5. What’s
Reality
Mining?
Computer Social Science
Social Physics
Social Network Patterns

6. “
We define reality mining as quantifying and modeling
long-term human behavior and social interactions,
by using mobile phones and wearable badges
as sensors that capture realworld
face-to-face interactions.
Reality Mining and Personal Privacy Will Privacy Disappear when Social Sensors Learn Our Lives? - MIT Media
Laboratory

8. Computer Science
- Data Mining
- Dynamic Networks
- Behaviour Analysis
- Machine Learning
Methods
- Statistical Analyses
Reality Mining
Social Science
- Behaviour Analysis
- Psychologic Social
- Polytics and
Economic Analysis
- Health
- Nature Analysis

9. Motivation
Human BehaviorPatterns Machine Learning
Patterns Human Behavior Machine Learning

10. Examples
Some examples using
Reality Mining

11. Terrorist Tracking
India

12. Predicting Congestion
Traffic congestion predicted using mobile phone GPS data

13. Analyses the flow face-to-face and producvity
Patterns of communication within departments of a bank

14. Reality Mining data from GPS
Patterns of human movement in San Francisco Limited mixing among people with different
behavior patterns

15. Health – Indentify depression
Types speech and Variations : Pitch, variability of pitch and energy

16. Research
Experiments

17. Modeling social diffusion using mobile phones
Shows the pattern of proximity between people during one day

18. Modeling social diffusion using mobile phones
Shows that different social relationships are associated with different patterns of proximity

19. Classifying spending behavior using socio-mobile data

20. Reality Mining and Social Network in nature

21. Many Researches
- Efficient detection of contagious outbreaks in massive metropolitan
encounter networks
- The Social Amplifier – Reaction of Human Communities to Emergencies
- Friends don’t Lie - Inferring Personality Traits from Social Network
Structure
- Limits of social mobilization
- Incremental Learning with Accuracy Prediction of Social and Individual
Properties from Mobile-Phone Data
- The capacity to collect and analyze massive amounts of data
unambiguously

22. Challenges
Problems and
solutions

23. Challenges
- Security, Data ownership and privacy
- Echo Chamber in network
- Robust models of collaboration and data sharing between industry and
the academy need to be developed that safeguard
- How to developing technologies that protect privacy while preserving
data essential for research?
- How to integrate and approach Computer Science Scientists and Social
Scientists?
- Developer robust algorithms to process the big data around the world

24. Conclusion
Some considerations

25. Conclusion
- New way for understand to social mechanism and life
- Improving methods to sharing private data and privacy policies
- Advanced analysing big data around the world
- Increase the efficiency and responsiveness of industries and
governments.
- Convenience for everything at today
- Computational social science needs to be the work of teams of social
and computer scientists.
- Salvation or our destruction.

26. Thanks!
Any questions?
You can find me at:
virgiliosolano.wordpress.com
virgiliosolmag@gmail.com

27. References
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Laboratory.
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779-782.
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Wearable Sensing Technology.” Acadmey of Mangement Annual Conference. Anaheim, CA, USA.
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Page. Electronic Privacy Information Center, 11 September 2008. Retrieved 14 July 2009
- A. Pentland. (2008) “Reality Mining of Mobile Communications: Toward a New Deal on Data”,
http://hd.media.mit.edu/wef_globalit.pdf, April;
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Reality Mining”. http://hd.media.mit.edu/rwjf-reality-mining-whitepaper-0309.pdf, April.

28. References
- N. Eagle and A. Pentland, (2009) “Reality Mining: Sensing Complex Social Systems”, Personal and Ubiquitous
Computing, Vol 10, #4, 255-26.
- N. Eagle and A. Pentland (2009) “Employee Monitoring: Is There Privacy in the Workplace?” Fact Sheet 7:
Workplace Privacy. Privacy Rights Clearinghouse, April.
- A. Madan, B. N. Waber, M. Ding, P. Kominers, and A. Pentland (2009) “Reality Mining and Personal Privacy”:
Will Privacy Disappear when Social Sensors Learn Our Lives?,
http://senseable.mit.edu/engagingdata/papers/ED_SIII_Reality_Mining_and_Personal_Privacy.pdf, April.
- J. Krause, S. Krause, R. Arlinghaus, I. Psorakis, S. Roberts and C. Rutz (2013) “Reality mining of animal social
systems”. http://www.igb-berlin.de/tl_files/data_igb/_aktuell_presse/_publikationen/KrauseEtAl_TREE_2013.pdf,
April.