Blogs

The third presenter in this Web Science 2016 session is Tu Ngoc Nguyen, who reintroduces us to the Internet Archive's Wayback Machine. This is a useful service, but searching it is not necessarily straightforward. Is it possible to draw on the non-content features to improve search results?

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 next WebSci 2016 paper session starts with a presentation by Pei Zhang, which introduces what she calls the Content-Linking-Context model, or CLC. The context for this is legislation such as the Digital Millennium Copyright Act (DMCA) and the European e-Commerce Directive, as well as various national legislation in the EU.

The final speaker in this WebSci 2016 session is Ramine Tinati, whose focus is on citizen science platforms. Citizen science itself has been around for hundreds of years, but more recent developments in online crowdsourcing techniques have enabled even greater mass participation in such scientific activities; one early success in this was Zooniverse, which asks users for help in classifying galaxy types.

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.

The next paper in this WebSci 2016 session is presented by Mariana Arantes, whose interest is in the matching of video ads to YouTube videos. Such ads are displayed before some YouTube videos, and they can often be stopped after a set number of seconds. How do users consume these ads? How does their popularity change over time? What is the relationship between videos and ads, and does a better content match mean that ads are more likely to be watched all the way through?

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!