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How Twitter Network Features Predict Users' Attitudes towards Islam

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.

Social Media Campaigns to Encourage Environmentally Responsible Behaviour

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?

How Trolls Emerge: Do Community Evaluations Generate Negativity?

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.

Non-Content Features of Material in the Internet Archive

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?

Current Practices in Social Media Data Sharing between Researchers

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.

Automated Assessment of the Validity of Content Take-Down Notices?

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.

Motivations for Participating in Gamified Citizen Science Projects

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.

Behavioural Differences between Teen and Adult Instagram Users

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.

Patterns of YouTube Video Ad Consumption

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?


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