Observing Social Machines Part 1: What to Observe?
This document discusses observing social machines. It defines social machines as systems composed of people and technologies interacting with each other. The document examines what aspects of social machines could be observed, such as logs, analytics, and qualitative studies of user motivation. It discusses different observation techniques and scales. The document also provides examples of social machines like myExperiment and how they could be analyzed. It suggests identifying ecosystems where social machines interact and evolve to better understand their design processes and emergent behaviors. The document concludes by encouraging the audience to engage with and help design social machines while sharing methodologies, with the goal of continuing to study these systems.
Observing Social Machines Part 1: What to Observe?
- 1. David De Roure Observing Social Machines Part 1 What to Observe? Clare Hooper Megan Meredith-Lobay Kevin Page Ségolène Tarte Don Cruickshank Cat De Roure
- 3. Nigel Shadbolt et al
- 5. myExperiment is a Social Machine protected by the reCAPTCHA Social Machine
- 10. Whither the Social Machine?
- 11. Whither the Social Machine?
- 12. Whither the Social Machine?
- 13. What to observe? Logs Analytics Data findings e.g. Success rate of transcription Social sciences Qualitative study Motivation Individual and group Mixed methods Differences in technique and scale Unlikely to be an simple transferable metric
- 14. Trajectories
- 15. Trajectories... distinguished by purpose
- 16. Trajectories... distinguished by purpose Trajectories through Social Machines https://sites.google.com/site/bwebobs13/
- 17. Cat De Roure The Befriending of Raspberry Tree
- 18. • Are the tree, bot and/or dating site Social Machines? • What are their trajectories? • Cyberphysical scenario involving machine-to-machine communication without human mediation • Illustrates automatic assembly – unintended but purposeful • Glimpse of APIs and the service-oriented ecosystem • Bot detection algorithm illustrates observation mechanism (human / automated?) • Machines impersonating people; e.g. people can buy twitter followers, how do they know they’re not bots? The Lessons of the Raspberry Tree
- 21. Identify Ecosystems Where Social Machines are 1. Interacting and competing with others 2. Being designed, born and co-evolving 3. Variable in size, purpose, lifetime and intent 4. Reflecting the trends towards cyber-physical and machine-to-machine systems
- 22. • The constituent Social Machines and their trajectories • Technologies, humans and their interfaces, including intersection with physical world • The design processes, and how they correlate with successful machines • Ground rules leading to emergent behaviour – rules by which people abide – rules encoded in design – part of community conduct – grounded in how other Social Machines behave Analyse
- 23. • Observing Social Machines Part 2: How to Observe? • Toolkit approach • Embrace other observatories • Instrument the ecosystem Future Work
- 25. • You are all observers… • Go forth, engage with the machines • Design new ones! • The observatory is really a laboratory • Share the (methodological) toolkit • Report your findings in these workshops Your mission should you choose to accept it…
- 27. david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder www.scilogs.com/eresearch @dder SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org