On Data-Driven AI – and science…

Over the past few weeks I have been interviewed by the New Scientist and the BBC on the nature of modern Artificial Intelligence. This is about the data-driven nature of current intelligent systems, and how science is adopting a similar approach, and what implications this could have for society. Links follow…

New Scientist Article – needs subscription
Same article entirely readable on The Age (Australia)

Patterns in Media Content

Big Data Analysis of News and Social Media Content

Ilias Flaounas, Saatviga Sudhahar, Thomas Lansdall-Welfare, Elena Hensiger, Nello Cristianini (*)

Intelligent Systems Laboratory, University of Bristol

(*) corresponding author

The analysis of media content has been central in social sciences, due to the key role that media plays in shaping public opinion. This kind of analysis typically relies on the preliminary coding of the text being examined, a step that involves reading and annotating it, and that limits the sizes of the corpora that can be analysed. The use of modern technologies from Artificial Intelligence allows researchers to automate the process of applying different codes in the same text. Computational technologies also enable the automation of data collection, preparation, management and visualisation. This provides opportunities for performing massive scale investigations, real time monitoring, and system-level modelling of the global media system. The present article reviews the work performed by the Intelligent Systems Laboratory in Bristol University towards this direction. We describe how the analysis of Twitter content can reveal mood changes in entire populations, how the political relations among US leaders can be extracted from large corpora, how we can determine what news people really want to read, how gender-bias and writing-style in articles change among different outlets, and what EU news outlets can tell us about cultural similarities in Europe. Most importantly, this survey aims to demonstrate some of the steps that can be automated, allowing researchers to access macroscopic patterns that would be otherwise out of reach.

Download the survey article with pointers to the peer reviewed publications