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Panel presentation at ECDL 2009
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Panel presentation at ECDL 2009

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  • barriers to direct collaboration - time, geography, limited technology
    people as entry points
  • ‘liking’ items in FriendFeed being used successfully by chemists and other scientists to rapidly explore topics of interest
    more subtle ‘gestures’ - e.g. the ‘re-tweet’ in Twitter which points as much to the originator as it does to the resource or assertion they might have made
  • the AOL search data scandal in 2006 was widely reported as a breach of privacy, which it clearly was. But it also hinted at the extent to which users are increasingly present in systems. human richness of presence within these systems - not just data!!
    User 927 was identified by some as some disturbing ‘attention’ patterns were revealed - but what was very interesting about this user record is that is appears to represent an account shared by three very different people. Works with Amazon because there is a credit card involved - in academia need incentive for not sharing
  • is this a bug or a feature? people rather than representations of patterns of behaviour
  • is it better to really satisfy niche networks with tailored services, rather than a generalised offering which doesn’t really satisfy anyone equally?
    Barrier to creating bespoke apps has been reduced
  • Transcript

    • 1. (representations of patterns of aggregated usage) OR (people) Paul Walk Technical Manager p.walk@ukoln.ac.uk UKOLN is supported by: www.ukoln.ac.uk A centre of expertise in digital information management 1
    • 2. people in systems • (representations of patterns of aggregated usage) OR (people) • the development of data-centric approaches and services which provide scholars with resources targeted to their individual, personalised needs • pervasive networking technologies which have reduced some barriers to collaboration 2
    • 3. resource discovery via other people • ‘gestures’ indicating attention - enlightened self-interest • direct human ‘presence’ rather than anonymous or algorithmic actor • is this what we mean by ‘digital society’? • can a well developed & highly available social network reduce ‘filter-failure’? 3
    • 4. however.... • the scholar is not always in a ‘social’ mood - they might be: • collaborative • competitive • neutral • much of recent technology-enhanced collaborative enterprise depends on enlightened self-interest.... • ...this might not always apply • will people demand more control over their own attention data? • what do you know about me? • can I reuse this data myself elsewhere? • can I remove it from your system? 4
    • 5. lessons from AOL • anonymised user 4417749.... • ...or as her friends know her, Thelma Arnold • user 927 5
    • 6. anonymity? • David said ‘… and the more we track, the better we can adapt our service without your intervention.’ • but: • perhaps I welcome the chance to intervene • perhaps I want other people, known to me, to be able to intervene on my behalf 6
    • 7. niche/specialist networks • recommendation systems being tried with some apparent benefits being realised at undergraduate level • but, in academia, beyond undergraduate it’s long tail all the way! • small networks based on people actually knowing each other • do academics work this way? • economic/business drivers underpinning service design may be changing 7
    • 8. questions for service providers • what can those who provide digital library services offer the “long-tail” of academia? • in which context(s) might the personal/social network offer a good approach to resource discovery? • when might the service built on aggregated, anonymised attention data be appropriate? • can these approached be integrated by service providers, or is this task best left to the user or another agent? 8

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