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.

I was fortunate enough to attend the Computer Mediated Social Sense-Making workshop, conveniently situated on the ground floor of the building I work in, on the 14th of February.

Whilst more technical than the Digital Methods conference I went to in December, the talks and panel sessions served to build upon things I started to think about then.  Namely, beginning to situate my research interests amongst many concepts from the currently quite alien fields of sociology and anthropology.

The talks were varied, and key themes that emerged were the collection/use of data for social improvement (health and wellbeing, teaching and learning, disaster recovery), and the importance of context in making collected data genuinely useful.  A notable challenge is that one piece of data might have a thousand different contexts from the perspectives of a thousand different human beings.  So how to communicate these variations to software that processes this data, and perhaps makes decisions using it?

Perhaps not to worry too much about that at all.  Process things locally instead of globally, using local contexts and understandings, but make sure everything is annotated such that information can still be exchanged across the whole network, and differences in understanding can be accounted for or reasoned out if a need occurs.

For the record, I'm looking at how Semantic Web technologies could be used to better connect human and machine in the context of amateur digital content creation (movies, comics, music, art), including how semantically annotating creative (often collaborative) processes as well as the end products of these processes and the engagement of an audience with these products, could improve the overall experience of creating content (along a number of dimensions).  A massive part of this will be creating tools that actually collect the necessary data from users.  Ultimately, these tools will need to be invisible, ie. easily integrated into existing online routines, with no effort required to use them for the non-technically minded so that a network effect can take place.

Incentives for crowdsourcing came up during CMSSM, and someone pointed out that by gamifying data collection for research projects, incentives become the same as ones offered by gambling companies; something competitive and potentially addictive.  I think things like global systems of reputation and trust are useful on a network where people are to share data about their own work (or opinions of the work of others) and may be nurturing a desire for popularity or exposure on the network (a network where the people are central, because the data could not exist without them, but where the users and the data are simultaneously co-dependant).

Anyway, I'm still brainstorming.