SOCIAM Social Machines and Linked Data

Dominic DiFranzo Missing Piece

e-infrastructure

Big Data The Future! The Fourth Big Compute Quadrant

Convenonal Social online Computaon Networking R&D More machines

More people e-infrastructure

Big Data The Future! Social Big Compute Machines

Convenonal 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 evoluonary 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. collecve acon More machines

More people Social Machines the New Froner An Example

The Kenyan elecon 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 et al Social Machines are NOT Turing Machines

• they do contain convenonal algorithmic components • but much else is different • a will start with an incomplete specificaon that grows and evolves to cover more of the problem via interacon • a social machine achieves parcipaon through local incenves which become reinforced as the… • incenve for an individual to supply data to the algorithm increases as more individuals parcipate • a social machine has a noon of completeness that is a social rather than mathemacal issue • a social machine will not usually have a noon 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 • • Variees of data Time cricality • • 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 Tim Berners-Lee mc schraefel Luc Moreau

David De Roure

David Robertson

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 disrupve 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 sll have ulity; • what signals need to be observed, what is a fair and faithful sample of Web behaviour; • this is likely to call aenon to appropriate governance, ethical and legal issues. Web as lens

Web as arfact

Web Observatories hp://www.w3.org/community/webobservatory/ An observatory is sociotechnical

Technical Social • Mulple observatories • Researchers • Datasets and dataflows • Managers • Analycs • Shared artefacts • Tools and soware (intermediate results, • Automaon annotaon) • Noficaon (real me) • Shared methods • Provenance • Collaborave analysis • Secure • Policy • Linkable • Responsible innovaon • Programmac access • Training • Visualisaon (eyepiece?) • Standards!

Narrave

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 analycs

ongoing collecon

“Non-Consumpve 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 acvity 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 (hp://en.wikipedia.org/wiki/List_of_major_power_outages) could show us that. Recording of Provenance

• One of the largest difficules of the social science is reproducibility of experiments. • Recording of provenance of the workflows of how experiments were done (what models, what dataset, what analycs, etc) could help solve this

Recording of Provenance

• With the more subjecve and interpreve 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

• Converng SOCIAM data using PRIZMS – Both large Qual and Quant data (Twier/ Zooniverse) • Leverage our twier ontology and tools to SOCIAM twier data. Test how reusable our tools and demos are on other large scale twier data • Building and collecng analycal tools for both Qualitave and Quantave researchers.