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How big data can be used to understand major events

Press release issued: 4 March 2015

With the most unpredictable UK general election looming in modern times, how can big data be used to understand how elections are covered by the media? New research by the University of Bristol has for the first time analysed over 130,000 online news articles to find out how the 2012 US presidential election played out in the media.

Academics from the University’s Intelligent Systems Laboratory (ISL), led by Nello Cristianni, Professor of Artificial Intelligence, analysed mass media coverage from the 2012 US presidential election using big data.  The paper is published in the journal Big Data & Society.

This is the first study in which political positions have been automatically obtained from a very large amount of online news.  The system created goes well beyond traditional word-association networks with the use of richer linguistic analysis of texts.

By analysing the individual campaigns of the main presidential candidates, Obama and Romney, the research team studied how each campaign was represented in the media and found one of the key issues covered by the media during the 2012 campaign was Obama’s defence of his record on economic policy.

The findings suggest the issues of the Democratic Party the media focused on were the US economy and civil rights.  Overall, media reporting contained more frequent positive statements about the Democrats than the Republicans.  The Republicans were also more often the focus of negative statements by Democrats and other players. 

The study found the Republicans had more divisive opinions on issues compared to the Democrats.  The most contentious subjects in the campaign were the economic split between the two camps of tax breaks and the economy, and the split over gay marriage. 

Saatviga Sudhahar, Research Assistant in Machine Learning in the ISL and Department of Computer Science, and the main researcher on the project, said: “Mapping the full electoral campaign coverage by offline and online media is a very difficult challenge, given the large amount of data and the large number of sources available in advanced democracies.

“We believe that the methodology used for the study is a big step forward in the linguistic analysis of texts by using extracted relational data and could help us understand major events.”

The research team used a semantic graph that analysed the text and linked it to identified noun phrases and verbs.  The subject-verb-object triplets were then used as building blocks for a network. This method has never been applied to a real-world dataset on this scale and millions of documents were analysed to complete the study.

By using media data and having relationships in a graph, the researchers uncovered a unique mixture of endorsements and disapproval to represent the Republican and Democratic camps.

The research team found that the range of political positions can be reliably recovered from the set of claims attributed to each actor by the media reporting. The split of the network into the two main camps provides strong evidence that the main political relations can be found by using this approach.


Automated analysis of the US presidential elections using big data and network analysis’ by Sudhahar S, Veltri GA and Cristianini N in Big Data & Society.

Further information about the study, together with together with high-res images, is available at

Further information

The research is part of the ThinkBIG EU project led by Professor Nello Cristianini from the Intelligent Systems Laboratory at the University of Bristol.

Nello Cristianini is Professor of Artificial Intelligence at the University of Bristol.  His current research covers the large scale analysis of media content (news and social media), using various AI methods, and the implications of Big Data.

Professor Cristianini is the co-author of two widely known books in machine learning, “An Introduction to Support Vector Machines” and “Kernel Methods for Pattern Analysis” and of a book in bioinformatics “Introduction to Computational Genomics”.

He is also a former recipient of the Royal Society Wolfson Research Merit Award and a current holder of a European Research Council Advanced Grant.

About the Intelligent Systems Laboratory (ISL)
The University of Bristol’s Intelligent Systems Laboratory (ISL) is in the Merchant Venturers School of Engineering.

The University has a long tradition of excellence in Artificial Intelligence, with research groups in Engineering dating back to the 1970s and 1980s.

Research activities at the ISL include foundational work in machine learning (many of the ISL members work in this central area of research), and applications to web intelligence, machine translation, bioinformatics, semantic image analysis, robotics, as well as natural intelligent systems.

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