A quick ponder into visitor generated content and value classification.
Twitter has introduced new metadata for tweets; with the objective of helping developers filter out the most “valuable” tweets. This immediately got me thinking about visitor generated content (VGC) in museums. My PhD is grappling with the idea of impact and how you can go about measuring impact of VGC on museum experience. Over the past couple of years working on QRator and the Social Interpretation project, it has become clear that VGC, impact and value are notoriously difficult to define, interpret and well basically study.
In essence, Twitter is going to be introducing new metadata for Tweets so that you will receive tweets tagged up with value levels; initially just no value, low and medium. No High value tweets just yet. The aim is to make it easier for developers to surface what is arguably the better and more interesting content from otherwise noisy or high volume tweet streams.
We had a similar problem with the Social Interpretation VGC in particular, a high volume of visitor comments, and no clear way of moderating, categorising or “valuing” the better quality visitor comments. As with most high volume unstructured data, finding and highlighting the signal out of the noise can represent a significant challenge.
The problem is that “value” is highly subjective and varies on the context within which it is being consumed. One visitor’s value is not the same as the next. Nor is it likely to match what the museum defines as adding value. The SI team at IWM experimented with gardening comments, but we didn’t come up with a criterion to work from, so it was up to the moderator at the time to decide. At the Grant Museum with QRator we are trying to come up with criteria to look at the visitor answers to the current questions, but this is after the point of visitor contribution, and is very much based on the museum’s perceived value of the response. Is value something you add in the post moderation stage? Who’s value? The visitors or the museums?
So, is Twitter’s new value algorithm something that can be used by museums to classify VGC?
If I’m honest, no I don’t think it is. Is it really possible to create an algorithm that can classify value of comments? Surely value is judged by the reader? Can an automated system really evaluate subjective factors and identify the most valuable conversations for each individual? Doubtful.
But I will be watching how Twitter deals with concepts of value of tweets with interest.