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Social Media

A First Look at the Political Uses of Quote Retweets

Next up at Web Science 2016 is Yelena Mejova, who presents a paper on the new 'quote retweet' feature that Twitter introduced in April 2015. This form of retweeting includes the retweeted tweet as a URL in the retweet, and can be used for somewhat different purposes from other forms of retweeting: while button retweets may imply an endorsement of the original message, the substantial space for including the retweeter's views in a quote retweet might be used for more critical engagement with the quoted material, for instance.

Patterns of Engagement with Journalists' Tweets in Ireland

Next up at Web Science 2016 is Claudia Orellana-Rodriguez, whose interest is in how journalists spread the news on Twitter. Journalists now regularly engage on social media platforms, but there still is only a very limited understanding of how platforms like Twitter can be used most effectively.

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.

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.

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.

Identifying MOOC Learners on Social Media Platforms

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?

Web Science and Biases in Big Data

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.


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