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SOCIAM Social Machines and Linked Data SOCIAM Social Machines and Linked Data Dominic DiFranzo Missing Piece e-inFrastructure Big Data The Future! The Fourth Big Compute Quadrant Conven@onal Social online Computaon Networking R&D More machines More people e-inFrastructure Big Data The Future! Social Big Compute Machines Conven@onal Social online Computaon Networking R&D More machines More people The Order oF Social Machines Real liFe is and must be Full oF all kinds oF social constraint – the very processes From which society arises. Computers can help iF we use them to create abstract social machines on the Web: processes in which the people do the creave work and the machine does the administraon… The stage is set For an evolu@onary growth oF new social engines. Berners-Lee, Weaving the Web, 1999 A Big Picture Machines using people e.g. turk People using machines e.g. collec@ve ac@on More machines More people Social Machines the New Fron@er An Example The Kenyan elec@on on the 27th December 2007 resulted in a wave oF riots, killings and turmoil… AFrican blogger Erik Hersman read a post by another blogger Ory Okolloh… Birth oF “Nobody Knows Everything, but Everyone Knows Something.” Local observers submit reports using the Web or SMS Social Machine Variations D.C. "Snowmageddon" Fukushima Tsunami Port au Prince Earthquake Arab Spring Obama '08 Nigel Shadbolt et al Social Machines are NOT Turing Machines • they do contain conven@onal algorithmic components • but much else is different • a social machine will start with an incomplete specificaon that grows and evolves to cover more oF the problem via interac@on • a social machine achieves par@cipaon through local incen@ves which become reinForced as the… • incen@ve For an individual to supply data to the algorithm increases as more individuals par@cipate • a social machine has a no@on oF completeness that is a social rather than mathemacal issue • a social machine will not usually have a no@on oF the correct output or terminaon… rather it runs connuously The dimensions oF Social Machines – Social Machines vary depending on • Number oF people • Empowering of • Number oF machines individuals, groups or crowds • Scale oF data • • Varie@es oF data Time cri@cality • • Type oF machine problem Extent oF wide area solving communicaon • • Type oF human problem Need For urgent solving mobilizaon • Specificaon oF goal state Lead Investigators Principal Investigator 5 years 2012-17 Nigel Shadbolt Co-Investigators EP/J017728/1 Wendy Hall Tim Berners-Lee mc schraefel Luc Moreau David De Roure David Robertson Peter Buneman What will SOCIAM do Theme 6 Web Observatory • Understand Social Machines through an observatory that observes, monitors and classifies social machines - both those oF the project and more widely on the Web - as they evolve; • it will also act as an early warning Facility For new disrup@ve social machines elsewhere on the Web; • to understand how Social Machines reach @pping points, longitudinal observaonal data will reveal how they grow once launched; • whether they coalesce into larger machines or Fragment into micro machines that s@ll have u@lity; • what signals need to be observed, what is a Fair and FaithFul sample oF Web behaviour; • this is likely to call aen@on to appropriate governance, ethical and legal issues. Web as lens Web as ar@Fact Web Observatories hcp://www.w3.org/community/webobservatory/ An observatory is sociotechnical Technical Social • Mul@ple observatories • Researchers • Datasets and dataflows • Managers • Analy@cs • Shared arteFacts • Tools and sogware (intermediate results, • Automaon annotaon) • No@ficaon (real @me) • Shared methods • Provenance • Collaborave analysis • Secure • Policy • Linkable • Responsible innovaon • Programmac access • Training • Visualisaon (eyepiece?) • Standards! Narrave Database VS • Cause and Effect • Unordered List of • Connects seemingly Items unordered events • Organized for fast together search and retrieval Narrave Database Linked Data Enhancements Saturday, December 4, 2010 Anatomy oF an observatory Plug and Play Query Subscribe analy@cs ongoing collecon “Non-Consump@ve Research” Linked Social Data • Take everything we learned with LOGD – Provenance/metadata/Linking/Lightweight • Work Like the Web – AAA Principle – Many Truths – Open World Assumpon – Boom Up Bring Back Context to Data • The events seen in our Big datasets never happen on their own, but are linked to related events. • By linking Large Heterogeneous Data together, we can help capture the larger context oF the data we care about. • Allows us to test the correlaons and possible causaons oF the phenomenon we see in our data Bring Context Back • For exmaple: – In our large video game dataset, we see no ac@vity From people playing in Kansas in Early December 2007. Why? – Could it be that a large ice storm in December 6 had a huge power outage From the 6-12th in that area? – Linking to power outages dataset From Wikipedia(hcp://en.wikipedia.org/wiki/List_oF_major_power_outages) could show us that. Recording oF Provenance • One oF the largest difficul@es oF the social science is reproducibility oF experiments. • Recording oF provenance oF the workflows oF how experiments were done (what models, what dataset, what analy@cs, etc) could help solve this Recording oF Provenance • With the more subjec@ve and interpre@ve nature oF Linked Data, it becomes vital that we know WHO said WHAT. • We need to record the authors oF Data to know how we should use it, and how it should be accepted. Plug and play observatories Technical and business interFace Current Work • Conver@ng SOCIAM data using PRIZMS – Both large Qual and Quant data (Twicer/ Zooniverse) • Leverage our twicer ontology and tools to SOCIAM twicer data. Test how reusable our tools and demos are on other large scale twicer data • Building and collec@ng analy@cal tools For both Qualitave and Quan@tave researchers. .
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