You are here

'Big Data'

Intersections between Follower and @mention Networks in the Australian Twittersphere

The next paper in this Social Media and Society session is by my QUT colleague Brenda Moon and me. Our work-in-progress presentation explores how we can connect our long-term data on the structures of follower networks in the Australian Twittersphere with shorter-term comprehensive information on actual posting activity; we are interested how follower networks and @mention networks cross-influence each other. What emerges already from our preliminary work is that different communities of Australian Twitter users appear to exhibit some very different activity patterns, and that some appear more likely to break out of their follower/followee network clusters than others. One of the newer Twitter communities in Australia, teen users, seem to tweet particularly differently from the others.

Slides are below:

One Day in the Life of a National Twittersphere from Axel Bruns

The Emotional Dimension of Data Visualisation

It's the final day at Social Media and Society, and today's keynote is by Helen Kennedy. She's beginning with the question of how data make us feel: and it is a question that reflects the power of data, of metrics, in an environment where such data have become ordinary and everyday. As data mining becomes more commonplace, new data relations emerge, and these are increasingly characterised by emotion as much as by rationality. This represents a desire for numbers, and points to some of the contradictions that include a hunger for as well as a criticism of numbers. How we get through the visualisation of numbers also plays an important role.

Social media data form part of a larger ecosystem of connective data in this context. Social media data mining has also become ordinary – and should such ordinary forms of social media data mining concern us in the same way that more exceptional data (such as the Snowden leaks) should concern us? Helen has examined this in close engagement with social media data generators and users, and this has also uncovered the visceral reactions that people have when they encounter a data visualisation. Such responses to 'seeing data' also deserve further research: how do we live with data from the bottom up?

Affective Publics around the European Refugee Crisis and Paris Attacks

The final speaker in this Social Media and Society session is Moses Boudourides, who presents a study of the affective publics on Twitter surrounding the European refugee crisis and the Paris terrorist attacks. The project tracked some twenty keywords and hashtags relating to the refugee crisis, capturing a substantial volume of tweets that were further processed using Python.

Discussing Illicit Drug Use on Social Media

The third speaker in this Social Media and Society session is Alexia Maddox, whose interest is in the study of online discussions of illicit drug use. Illicit drugs are a stigmatised topic, which has pushed discussion into more permissive spaces such as social media. In Australia, there has been a renewed push to legalise medicinal marijuana, which has increased the volume of discussions about the drug, and this provides the current context for this study.

Tracking the 2012 U.S. Presidential Election on Twitter

The next speaker at Social Media and Society is Christopher Mascaro, whose interest is in 'big data' on political communication online. Political discourse studies have traditionally been restrained by geographic and social access, and 'big data' from online activities can overcome some of these barriers; it also introduces some new limitations that must be considered, however.

Reviewing the Emerging 'Big Social Data' Research

The next session at Social Media and Society is on 'big data', and begins with Andra Siibak (who is also the programme chair for AoIR 2017 in Tartu, Estonia!). She highlights the possible methodological shifts that arise from the use of 'big data' in social science research: this is in part seen as a shift towards more quantitative methods, but also as a more nuanced and methodological shift from designed to more 'organic' data, whatever we may mean by this. Approaches that are built on formulating and testing preconceived hypotheses may also be challenged by other, alternative approaches.

Social Media in Research: From 'Big Data' to 'Wide Data'

It's the second day of Social Media and Society in London, and after a day of workshops we're now starting the conference proper with a keynote by Susan Halford. She begins by pointing out the significant impact of social media on a wide range of areas of public and everyday life. We're constantly presented with the digital traces of social media – with social media data at an unprecedented scale, telling us something about what people do with social media in their everyday lives. This is an unexpected gift, but is also causing significant concern and scepticism.

What is the quality of the data – what are they, what do they represent, what claims can be made from these data? Some social scientists are even suggesting that such data are dangerous and can affect the public reputation of the scientists and disciplines using them. Few people were experts in working with social media data when these data first arrived – we are building the boat as we row it, to use an old Norwegian saying, and we're learning about how to do so as we go along.

Social Media and Collective Political Action

The closing (!) keynote of Web Science 2016 is presented by Helen Margetts from the Oxford Internet Institute. Her focus is on the use of social media for collective political action – that is, for activities undertaken by citizens with the aim of contributing to the public good. There is a strong feeling that such action is happening, but as yet not enough empirical evidence about how and why it is happening.

Even those who refuse to participate online are somehow caught up in the changes that the Internet has contributed to: our lives are intertwined with its technologies, platforms, and content. And these technosocial spaces also generate a substantial amount of transactional data about user participation that goes well beyond the sort of data – for instance about political attitudes and engagement – that were available in pre-Internet days.

Modelling Discrete Choice Problems

Post-lunch, the final day of Web Science 2016 continues with a keynote by Andrew Tomkins, whose focus is on the dynamics of choice in online environments. He begins by highlighting R. Duncan Luce's work, including his Axiom of Choice, but also points out the subsequent work that has further extended the methods for analysing discrete choice. Today, the most powerful models are mathematically complex and computationally intractable, as well as requiring sophisticated external representations of dependence.

From this work it has become clear that the Axiom of Choice holds only under relatively select conditions. Contextual data is of great importance here, and additional approaches to modelling general behaviour of discrete choice are required. The Randomised Utility Model, for instance, assigns a random utility value to each available choice, and in an ideal world users would then select the item with maximum utility; but because of existing preferences real-world users will deviate from such choices.

How Facebook Uses Computational Processes to Police Its Ads

The final Web Science 2016 keynote for today is by Daniel Olmedilla, whose work at Facebook is to police the ads being posted on the site. Ads are the only part of Facebook where inherently unsolicited content is pushed to users, so the quality of those ads is crucial – users will want relevant and engaging content, while advertisers need to see a return on investment. Facebook itself must ensure that its business remains scalable and sustainable.

Key problem categories are legally prohibited content (e.g. ads for illegal drugs); shocking and scary content; sexually suggestive material; violent and confronting content; offensive before-and-after images; ads with inappropriate language; and images containing a large amount of text.

Pages

Subscribe to RSS - 'Big Data'