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).

It looks like 2016 is destined to start with a bang rather than a whimper: I’m delighted to announce that a major collection I’ve edited with my colleagues Gunn Enli, Eli Skogerbø, Anders Olof Larsson, and Christian Christensen in Oslo and Stockholm has now been published. The Routledge Companion to Social Media and Politics is a 37-chapter, 560-page collection of current research on the uses of social media in political activism and electoral campaigning.

From Anonymous to the Scottish Independence Referendum, from oppositional politics in Azerbaijan to elections in Kenya, the Companion covers a broad range of social media uses and impacts. It combines this with a number of keystone chapters that review and update existing political communication theory for a social media context. My sincere thanks to our many contributors, my co-editors, and especially our hard-working editorial coordinator Nicki Hall for making this publication happen – hope you enjoy it!