reality mining
TRANSCRIPT
Reality Mining
Virgílio SolanoRA 180158
◎Universidade Estadual de Campinas
◎ Instituto de Computação◎ Web e Web Semântica
◎ Prof. André Santanchè
Agenda
◎ Introduction◎ What’s Reality Mining?◎ Motivation◎ Examples◎ Research◎ Challenges◎ Conclusion◎ Questions
What’sReality
Mining?Computer Social Science
Social Physics
Social Network Patterns
“We define reality mining as quantifying and modelinglong-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
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
Motivation
Human BehaviorPatterns Machine Learning
Patterns Human Behavior Machine Learning
ExamplesSome examples using
Reality Mining
Terrorist Tracking
India
Predicting Congestion
Traffic congestion predicted using mobile phone GPS data
Analyses the flow face-to-face and producvity
Patterns of communication within departments of a bank
Reality Mining data from GPS
Patterns of human movement in San Francisco Limited mixing among people with different behavior patterns
Health – Indentify depression
Types speech and Variations : Pitch, variability of pitch and energy
ResearchExperiments
Modeling social diffusion using mobile phones
Shows the pattern of proximity between people during one day
Modeling social diffusion using mobile phones
Shows that different social relationships are associated with different patterns of proximity
Classifying spending behavior using socio-mobile data
Reality Mining and Social Network in nature
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
Challenges
Problems and solutions
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
Conclusion
Some considerations
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.
References
- T. Choudhury (2004) “Sensing and Modeling Human Networks.” Cambridge, MA USA, PhD Thesis, MIT Media Laboratory.
- A. Pentland (2005) “Socially Aware Computation and Communication.” IEEE Computer,33-40.- F. Grippa, A. Zilli, R. Laubacher and P. Gloor (2006) “E-mail may not reflect the social network.” Proceedings of
the North American Association for Computational Social and Organizational Science Conference.- A. Pentland (2006) “Automatic mapping and modeling of human networks.”, Physica A: Statistical Mechanics
and its Applications. - A. Pentland (2006) “Life in the network: the coming age of computational social science”,
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2745217/, April;- A. Pentland (2006) “Automatic mapping and modeling of human networks.”, Physica A: Statistical Mechanics
and its Applications. - S. Aral, E. Brynjolfssen and M.W. Van Alstyne (2007) “Productivity Effects of Information Diffusion in Networks,”
MIT Center for Digital Business, paper 234 - L. Backstrom, C. Dwork, and J. Kleinberg (2007) “Wherefore Art Thou R3579X? Anonymized Social Networks,
Hidden Patterns, and Structural Steganography.” WWW Conference.- M. Gonzalez, C. Hidalgo and A.-L. Barabási (2008) “Understanding Human Mobility Patterns.” Nature 453, pp
779-782. - B.N. Waber, D. Olguin Olguin, T. Kim and A. Pentland (2008) “Understanding Organizational Behavior with
Wearable Sensing Technology.” Acadmey of Mangement Annual Conference. Anaheim, CA, USA. - B.N. Waber, D. Olguin Olguin, T. Kim and A. Pentland (2008) “Workplace Privacy.” EPIC Workplace Privacy
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;- D. Lazer, D. Brewer, T. Heibeck and A. Pentland. (2009) “Improving Public Health and Medicine by use of
Reality Mining”. http://hd.media.mit.edu/rwjf-reality-mining-whitepaper-0309.pdf, April.
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.