I had two meetings with Dave Robertson, my second supervisor, about what on earth I'm doing, and here is a vague summary of my thoughts afterwards.

I came to the realisation between meetings that I need to scrap the term Amateur Creative Digital Content, because amateur doesn't really apply by its true definition and creative is too subjective anyway.

Focus on content creators, not content (so previous point doesn't matter so much anyway; maybe just need to look at existing ways people are describing types of users to make it clear who I'm concentrating on).

In terms of emphasis of the thesis, I need to make a choice between taking a cognitive science/sociology perspective and a tecchie/engineering perspective (I choose tech because that's where I'm most comfortable, but the sociology side of things is still important).

(Therefore) I need to think concretely now about technology architecture.

Not to get too hung up on the Semantic Web; the technologies are a vehicle for testing theories, rather than an end in itself (though I still think facilitating a big linked data set of this sort of data is useful in the long run for research and practical applications, I didn't labour that point).

Social machines, and how Dave's process modelling language fits in, which I think I get in theory but not practice (I'd probably have to look at a working application and code to understand really). Some of the principles may be useful further down the line, but probably not the language itself or anything.

Technology-wise, I'm not thinking about anything novel or new, but more new ways for how various Web and SW technologies are combined and applied to this domain. (?)

So maybe the novelty is in marking up various things about content creators and using this to infer information about the processes they're involved in (or want to be involved in) in order to then facilitate these processes, without (necessarily) ever explicitly representing these processes (because from the content creators' perspective, they're certainly not thinking in terms of formal representations of processes, and in many cases won't know what they're trying to make until it's done, for example).

How to represent the inferences made might be novel and exciting, but I don't know.

Hmm, I still don't think I've figured out how to evaluate .. anything. Beyond comparing activities of users with magical-new-system vs without magical-new-system. And maybe, going back to the this-big-dataset-is-useful idea, by finding questions we can now ask about these kinds of communities that we couldn't before because they were so fragmented.

8th - 14th July

Semantic Web Summer School, much heat, much fun, much learning... Here's an index of my posts.

15th - 21st July

Friends visited.  Progress included writing notes to myself to figure out just what my PhD outcomes really are, and why.  Came up with:

1. Recommending how to usefully describe diverse amateur creative digital content (ACDC) using an ontology.
    a) What are the parts of ACDC that need to be represented?  Identify and categorise properties. How do these differentiate it from other similar content?
    b) What existing ontologies can be used to do this, and how do they need to be extended?
   
2. Building an initial set of linked data about ACDC, and providing means for its growth and use.
    a) Manual annotation of ACDC, and refinement (to test ontology).
    b) Tools for automatic annotation of the parts of ACDC that it is possible to automatically annotate.
    c) Tools for manual annotation by the community of content creators and consumers for the parts of ACDC that cannot be automatically annotated.
    d) Tools to expose the linked data for use by third-party applications.

3. Create and test an example service which uses the linked data to benefit content creators and/or consumers.
     eg. Unobtrusive recommendations for collaborative partners (most likely); content recommendation; content consumption analysis (like tracking viral content); community building / knowledge sharing in this domain; ... .

22nd - 28th July

Brainstormed with Ewan about stage 3 (above), and came up with the idea of an interface that allows content creators to allocate varying degrees of credit for roles played by different people when collaborating on a project.  This would serve to both gather collaborative bibliographic data, learn things about how different segments of the community allocate credit, and provide a potentially useful tool for content creators.  With the future value that, if we can learn enough to estimate role inputs from different people, it could be used for things like automatic revenue sharing.

Then spent the rest of the week in London, frolicking amongst the YouTubers (including attending a meeting at Google about secret YouTube-y stuff), and annotated some ACDC.  Write-up coming soon.

RELEVANT.

Users (consumers?):

• Finding content
• Media types * mostly text at the moment, little integration of different types
• Specific tasks - not much connection of results with user tasks.

More data than just what you seen in the media (cue my Venn diagram).

Plus, eg. paintings - lots of 'cultural baggage'.

Care more about the story than the media.
Interpretation by end users.  Hopefully message that the author intended.

Meaning of combination of assets.
eg. Exhibition of artists work.

Interacting further with the media.

• Search - serendipitous or focussed around a theme (or both).  Different search goals.
• Sharing, passing it on.

(SW and multimedia community need to work together).

-> Raphael Troncy on Friday - attaching semantics to multimedia on the Web.

Need mechanisms:

• to identify (parts of) media assets.
• associate metadata with a fragment.
• agree on meaning of metadata.
• enable meaningful structures to be composed, identified and annotated.

Workflow for multimedia applications

• Canonical processes of media production
• Reduced to the simplest form possible without loss of generality.

Heard of MPEG-7? Don't bother.. very much from a media algorithms perspective.

Applications:

• Feature extraction.
• News production.
• New media art.
• An interactive exhibit that responded to audience present.
• Hyper-video.
• Linked video.
• Photo book production (CeWe).
• (Using this example for explaining processes).
• Ambient multimedia systems with complex sensory networks.

Canonical processes overview...

There's a paper.

CeWe photobook - automatic selection, sorting and ordering of photos.
Context (timestamp, tags) analysis and content (colours, edges) analysis.

Things from these you want to represent your digital system (ie with LOD):

• Premediate, eg.
• remember to take your camera on holiday.
• write scripts, plan shots.
• place a security camera in the right location.
• Construct Message (not really in the chain, appears all over the place); what to conveny with media? Intention? eg.
• show people a great holiday.
• sell a product.
• inform/advise.
• Create (method of creation might be important, so record in metadata), eg.
• take photos.
• make video.
• Annotate, eg.
• automatic or manual.  Stuff that is embedded by device vendors (but there's so much more...)
• domain annotations: landscapes/portraits, timestamps, face recognition.
• Publish, eg.
• compose images into photobook.
• Distribute, eg.
• print photo book and post.
• cyclic processes online.

COMM - Core Ontology for Multimedia.

Premediate and construct message - human parts, she doesn't expect them to be digitised any time soon.

Using Semantics to create stories with media

Can we link media assets to existing linked data and use this to improve presentation?

How can annotations help?

• What can be expressed explicitly?
• Message (somewhere between a html page and poetry).
• Objects depicted.
• Domain information. <--- li="">
• Human communicaiton roles (discourse). <--- li="">

Vox Populi (PhD project)

Traditionally video documentary is a set of shots decided by director/editor.
vs.
Annotating video material and showing what the user asks to see.

interviewwithamerica.com

Annotations for these documentary clips:

• Rhetorical statement; argumentation model (documentary techniques).
• Descriptive (which questions asked, interviewee, filmic).
• Filmic: continuity like camera movements, framing, direction of speaker, lighting, sound - rules that film directors know.
• Statement encoding (eg. summary what the interviewee said):
• subject - modifier - object statements.
• Thesauri for terms.
• Can make a statement graph, finding which statements contradict and which agree.
• (He encoded this stuff by hand - automated techniques aren't good enough).
• Argumentation model - claims, concessions, contradictions, support.

Automatically generated coherant story.

• Are we more forgiving watching video? (Than reading these statements as text).  Peoples' own interpretations strongly affect understanding of the message.

Vox Populi has (not for human consumption) GUI for querying annotated video content.

User can determine subject and bias of presentation.
Documentary maker can just add in new videos and new annotations to easily generate new sequence options.


User informatio needs - Ana Carina Palumbo

Linked TV.  Enhancing experience of watching TV.  What users need to make decisions / inform opinions.

• Expert interviews (governance, broadcast).
• User interviews - what people thought they need (215 ppts).
• User experiments - what people actually need.

Experiment - oil worth the risk?

• eg. people wanted factual information from independent sources; what the benefits are; community scale information.

Published at EuroITV.

Conclusions

• We can give useful annotations to media access, useful at different stages of interactive access (not just search).
• Clarify intended message. Explicity with annotations.
• Manual or automatic.
• Media content and annotations can be passed among systems.
• No community agreement in how to do this. <--- li="">
• How to store?

Questions

Hand annotations are error prone - how to validate?
Media stuff - there can be uncertainty, people don't always care.

Motivating researchers to annotate...
Make a game.

Store whole video or segements?
W3C fragment identification standards - timestamps via URLs.

20th - 26th May

Continued to work on literature review.  Nothing much to report.

Went to MCM Expo in London and managed to find time (around non-stop merch selling for TomSka and Eddsworld) to ask between 30 and 40 content creators - a wide variety of ages, experience, types of content - about their process and collaborative practices.  The thing they all had in common (I randomly picked people as they were waiting in the two hour long queue to get autographs from Tom) was that they all do what they do because the love it, want to entertain people, and if the could earn a living from it too that would be amazing; but that's not why they do it.  For many it's the dream, but not one they expect realistically to achieve.

That is why this is important to me.  Because everybody should be able to make a living from doing what they love*, and the technology exists to allow it.  How exciting.

* Unless they're really bad at it.  There's only so much technology can do.  But they should definitely have the chance to get good before caving in to a ninetofive that they're not totally passionate about.