Category Archives: WSTNet News

Data Governance in Finance

John TaysomWeb Science Trust Board member, John Taysom writes on the Importance of Data Governance, with Special Reference to Finance in a recent Royal Society and British Academy publication. The report, Connecting debates on the governance of data and its uses, brings together contributions from leading representatives from academia, government and business; including experts in ethics, law, finance, social and data sciences, machine learning and statistics in order to build connections between existing debates, and identify key questions and gaps.

Download the report pdf 

The Academies have initiated a project examining new uses of data and their implications, and reviewing the data governance landscape. The project will make recommendations for cross-sectoral governance arrangements that can ensure the UK remains a world leader in this area.

Our Web Science Manifesto

For the Web to succeed, we need to understand its societal challenges including increased crime, the impact of social platforms and socio-economic discrimination, and we must work towards fairness, social inclusion, and open governance.

Wendy Hall
Professor Dame Wendy Hall, Managing Director, Web Science Trust

Jim Hendler
Professor Jim Hendler, Chair, Web Science Trust

Steffen Staab
Professor Steffen Staab, Chair, WSTNet

Web Science is even more important now than it was when the field was launched ten years ago, say Professors Dame Wendy Hall, Jim Hendler, and Steffen Staab in our Web Science Manifesto, published earlier this week at WebScience@10.

While recognising the huge influence the Web has had on our lives since its foundations were defined by Tim Berners-Lee 27 years ago, the Hall, Hendler, and Staab focus their attention on how Web Science tackles the  unforeseen social outcomes of this era-defining technical innovation. They discuss the digital divide that separates those who have and those who do not have access to the Web – the challenges we must understand to find a viable balance between data ownership and privacy protection, and between over-whelming surveillance and the prevention of terrorism.

To find out more read our Web Manifesto (pdf download).

Real-time Twitter Visualisations for the US 2016 Presidential Elections

Twitter Visualisation at RPI For the 2016 US Presidential election, researchers at the University of Southampton with support from the EPSRC funded project SOCIAM,  built a real-time data visualization that combined traditional polling data with social media posts. The application was built and designed for the Rensselaer Polytechnic Institute EMPAC Campfire, a novel multi-user, collaborative, immersive, computing interface that consist of a desk height panoramic screen and floor projection that users gather around and look into. The application is also a part of the Web Macroscope (a visualization platform developed at the University of Southampton) and uses data from the Southampton Web Observatory.

Data collection for the polling data was taking from the Huffington Post Pollster API, which collects all the popular polls and their results. The social media data was collected on Twitter, using both their Streaming and Search API. The Streaming API was used to create a stream of data that included 1% of all tweets that had any of the popular and official hashtags and words used by each campaign to show support for their candidate. This hashtag list included tags like ‘TeamTrump’, ‘maga’, ‘TeamTrump’, and ’draintheswamp’ in support for Donald Trump, and ‘LoveTrumpsHate’, ‘ImWithHer’, ‘StrongerTogether’, and ‘WhyIWantHillary’ in support for Hillary Clinton. Any tweets that mixed hashtags and words from both candidates were removed as this was normally done in a way to not show support for a candidate, but to react to supporters on the other side.Campfire visualisation of US election Twitter activity
Results from the visualizations showed different levels of support on Twitter for each candidates over time. In the days leading to the election on November 8th, tweets in support for Trump were 1.5 times greater than those in support for Clinton. Interestingly, on the day of the election, this ratio switched and levelled off. Around the 2pm EST on November 8th, tweets in support for Clinton were almost equal to the number of tweets supporting Trump. Later in the night of the election, the ratio of support changed again, with tweets in support of Trump being 1.14 times larger than those in support for Clinton.
Another interesting result from the data, was the how many tweets that had geographic information tagged to them were overwhelmingly in support for Clinton throughout the day leading and on the election. Most tweets streamed through the visualization had no GPS lat/long data embedded in them (these tweets often come from mobile phones using the Twitter App, with the optional GSP location data option enabled). As a whole, these geographic tweets are a small minority of the data collected from the Twitter Stream (about 1%). Interestingly, these geographic tweets supported Clinton 15 times more than Trump. Why this is the case is hard to say. It looks like Clinton supporters use mobile apps with location data more than Trump supporters.
Two other studies – one from researchers at USC, and another from Oxford University, the University of Washington and Corvinus University of Budapest,both showed that AI controlled bots were spreading pro-Trump content in overwhelming numbers. This created the illusion of more support for Trump on Twitter than make naturally been. Our results of geotagged tweets in support for Clinton, despite overall support from Trump on Twitter might be due to this issue of bots.
Authored by Dominic DiFranzo, 18 November 2016.

Two globally-renowned research institutes join WST Network

WSTNet – The Web Science Network of Laboratories – is delighted to announce that two globally-renowned research institutes have joined the network this month.

The Data Science Institute at Imperial College, London, and the Institut National de Recherche en Informatique et en Automatique (INRIA) both become full members of WSTNet, demonstrating the growing reach of the Network, and the long-term global significance of its collaborative focus in Web Science and Data Science.

The Data Science Institute at Imperial College is at the forefront of research in data science, working across Imperial College to support the deployment of cutting-edge technologies in wide-reaching data-driven research. The Institute hopes to expand its collaborations with the Web Science community, in particular to explore the value of big data technology for applications in social machines.

INRIA, the French National Institute for Computer Science and Applied Mathematics, promotes scientific excellence for technology transfer and society. INRIA’s 2600 employees – graduates from the world’s leading universities – rise to the challenges of digital sciences. With its open, agile model, INRIA is able to explore original approaches with its partners in industry and academic research, providing an effective response to the multidisciplinary and application challenges of digital transformation.

Established in 2010, the Web Science Network of Laboratories brings together some of the world’s outstanding academic researchers, based in 20 leading research institutes around the world. WSTNet aims to advance global research and education, outreach to industry, and the exchange of ideas. It supports collaborative research and education programmes, facilitates the exchange of researchers and graduate students, runs conferences, workshops and symposia, and aims fundamentally to advance the study and engineering of the Web for the benefit of society.

‘We are delighted to welcome our two new member institutions to WSTNet,’ said Professor Steffen Staab, Chair of the WSTNet Labs. ‘This is a rapidly expanding community, reflecting the significant impact of the Web on every aspect of our lives and futures.

‘Both Imperial College Data Science Institute and INRIA have already played eminent roles in advancing fundamental knowledge and technical approaches in their research areas. We look forward to the contribution they will make to WSTNet and to the high levels of engagement which will result from the increasing opportunities to share and advance our knowledge.’

Fabien Gandon of INRIA commented: ‘INRIA has actively supported the Web since its beginning, participating in the very first R&D that produced the Web in the early 1990s, and as a founding member of the W3 Consortium (W3C). Many of INRIA’s research teams contribute to computer science domains which are very relevant to the Web’s architecture, including data security and privacy enforcement, distributed and decentralized architectures, programming languages, natural language processing, and formal methods.’

Professor Yi-Ke Guo of the Data Science Institute at Imperial College, commented: ‘’The Web Science Network is committed to enlisting the help of research organisations working on the future of the Web. The development of data science will benefit greatly from this revolutionary research. We are excited to be part of the Web Science Network, and we are keen to contribute to this field to ensure the Web is even more integral to our lives.’