Day two of Web Science 2016 begins with a keynote by Jure Leskovec, whose interest is in antisocial behaviour in social media spaces. He begins by noting that the Web has moved from a document repository or library to a social space, where users contribute content and provide feedback to each other. Platforms for this include the main social media spaces, as well as Reddit, StackOverflow, and the comment sections of news sites.

These two metaphors for the Web – as a library and as a social space – are very different from each other, especially in how users are policed and controlled. In the latter model, one user's experience is a function of other users' experiences, and the question thus becomes how to keep users engaged and promote positive, constructive behaviour – and how to police the small groups of users who disrupt the community and have a disproportionately large effect on all other users' experiences.

And the final paper in this Web Science 2016 session is by Katrin Weller and me. Below are our slides and abstract, and a link to the full paper:

Twitter as a First Draft of the Present – and the Challenges of Preserving It for the Future from Axel Bruns

The next WebSci 2016 presenters are Katharina Kinder-Kurlanda and Katrin Weller, who argue that it is necessary to address the digital divides in data accessibility in social media research. They interviewed a large number of social media researchers, and what emerges from this work is that much data sharing is already taking place, but under varying circumstances.

The third speaker in this WebSci 2016 session is Dongwon Lee, whose interest is in user activity patterns on Instagram. The major finding of this study is that teens and adults exhibit different activity patterns, but just as important a contribution here is the methodological contribution to the study of Instagram that this paper makes.

We start the first paper session at WebSci 2016 with a paper by Guanliang Chen that examines learner engagement with Massively Open Online Courses (MOOCs). These generate a great deal of data about learner engagement during the MOOC itself, but there's very little information about learners before and after this experience. Can we use external social Web data to identify and profile these learners, in order to better customise the learning experience for them?

It's a cool morning in Germany, and I'm in Hannover for the opening of the 2016 Web Science conference, where later today my colleague Katrin Weller and I will present our paper calling for more efforts to preserve social media content as a first draft of the present. But we start with an opening keynote by Yahoo!'s Ricardo Baeza-Yates, on Data and Algorithmic Bias in the Web.

Ricardo begins by pointing out that all data have a built-in bias; additional bias is added in the data processing and interpretation. For instance, some researchers working with Twitter data then extrapolate across entire populations, although Twitter's demographics are not representative for the wider public. There are even biases in the process of measuring for bias.

I’m delighted to share a couple of new publications written with my esteemed colleagues in the QUT Digital Media Research Centre – and as if we weren’t working on enough research projects already, this year is about to get an awful lot busier soon, too. First, though, to the latest articles:

Axel Bruns, Brenda Moon, Avijit Paul, and Felix Münch. “Towards a Typology of Hashtag Publics: A Large-Scale Comparative Study of User Engagement across Trending Topics.” Communication Research and Practice 2.1 (2016): 20-46.

This article, in a great special issue of Communication Research and Practice on digital media research methods that was edited by my former PhD student Jonathon Hutchinson, updates my previous work with Stefan Stieglitz that explored some key metrics for a broad range of hashtag datasets and identified some possible types of hashtags using those metrics. In this new work, we find that the patterns we documented then still hold today, and add some further pointers towards other types of hashtags. We’re particularly thankful to our colleagues Jan Schmidt, Fabio Giglietto, Steven McDermott, Till Keyling, Xi Cui, Steffen Lemke, Isabella Peters, Athanasios Mazarakis, Yu-Chung Cheng, and Pailin Chen, who contributed some of their own datasets to our analysis.

Folker Hanusch and Axel Bruns. “Journalistic Branding on Twitter: A Representative Study of Australian Journalists’ Profile Descriptions.” Digital Journalism (2016).