Next up at Web Science 2016 is Walid Magdy, whose focus is on social media commentary following the terrorist attacks in Paris in late 2016. Immediately after the attacks, sympathy with Paris was expressed on Twitter – but as the attacks were linked with Islamist terrorists, anti-Muslim messages also began to appear.

Walid's team tracked relevant keywords and hashtags on Twitter after the attacks, and noted that more than one tenth of all messages discussed Islam in some form; of these, some 336,000 tweets were closely engaged with the question of Islam in Europe. The majority of these tended to …

The next session at Web Science 2016 is on information dissemination and engagement. It begins with a paper by Miriam Fernandez, whose focus is on promoting behavioural changes to combat climate change. Over the past years, there have been multiple social media campaigns that promote more environmentally responsible behaviours; what can these campaigns learn from theories of behaviour change, and how can these theories be translated into computational methods?

The focus here is on Earth Hour 15, and on the COP21 summit in Paris in 2015. The project draws on the 5 Doors Theory, which describes five stages of behavioural …

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 …

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

This paper provides a framework for understanding Twitter as a historical source. We address digital humanities scholars to enable the transfer of concepts from traditional source criticism to new media formats, and to encourage the preservation of Twitter as a cultural artefact. Twitter has established itself as a key social …

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.

From a methodological point of view, how can we document such sharing to ensure reproducibility? Legally, how can we make such sharing practices sustainable and non-infringing? Ethically, how can we ensure that such data sharing does no harm and lives up to the …

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?

The biggest challenge in this is to identify the learners across diverse social media platforms. There are some possible approaches: explicit matching based on public email …

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 …