My own paper starts the ICA-flavoured session at ECREA 2012 this afternoon; my presentation built on our research into the uses of Twitter to explore how we might reconceptualise the public sphere. The slides are below; audio will follow. now online, too.

Social Media, Big Data, and the Public Sphere from Axel Bruns

The final speaker (that went fast) at the CCC Symposium is Annette Markham, who begins by posing the question "What counts as data?" An answer to that question might provide an opportunity to bridge 'big data' and qualitative research - because what counts as data also defines what is considered to be viable, credible, or interesting findings.

We continue at the CCC Symposium with the great Alex Halavais, who is interested in the first place in the hidden patterns in data, and the learning - the evolution of ideas - which might result from them. But how do we detect such learning, such change? One indicator could be the popularity of content or users - success may be measured in the amount of attention received, for example.

The next speaker at the CCC Symposium is Christina Lioma, whose focus is on search engines. These, too, are repositories of data, but contain unstructured, heterogeneous, and noisy data - we're using them to find needles in haystacks (using various search logics, in fact: known needles in known haystacks, unknown needles in unknown haystacks, etc.). The discipline of information retrieval aims to develop theoretical principles for modifying and quantifying information and topical relevance.

My own paper started the post-lunch session at the CCC Symposium, discussing our Mapping Online Publics work in the field of Twitter research. I'll post up the slides and audio properly as soon as I can!

Slides and audio are below:

The next speaker at the CCC Symposium is Rasmussen Helles, who takes us back to the problem of 'big data'. Such data lend themselves well to visualisation, but this also creates substantial new problems as we make sense of data through their visual representations: we may see the patterns in the data, but we still don't necessarily know what they mean.