Submitted by Nello Cristianini on Fri, 27/04/2012 - 16:09
Large scale analysis of social media content allows for real time discovery of macro-scale patterns in public opinion and sentiment. In this paper we analyse a collection of 484 million tweets generated by more than 9.8 million users from the United Kingdom over the past 31 months, a period marked by economic downturn and some social tensions. Our findings, besides corroborating our choice of method for the detection of public mood, also present intriguing patterns that can be explained in terms of events and social changes. On the one hand, the time series we obtain show that periodic events such as Christmas and Halloween evoke similar mood patterns every year. On the other hand, we see that a significant increase in negative mood indicators coincide with the announcement of the cuts to public spending by the government, and that this effect is still lasting. We also detect events such as the riots of summer 2011, as well as a possible calming effect coinciding with the run up to the royal wedding.
REFERENCE:Thomas Lansdall-Welfare, Vasileios Lampos and Nello Cristianini: Effects of the Recession on Public Mood in the UK. Accepted for publication in the International Workshop on Social Media Applications in News and Entertainment (SMANE), 2012.
Vasileios Lampos and Nello Cristianini
Intelligent Systems Laboratory
University of Bristol
Abstract. Real time monitoring of environmental and social conditions is an important part of developing early warning of natural hazards such as epidemics and floods. Rather than relying on dedicated infrastructure, such as sensor networks, it is possible to gather valuable information by monitoring public communications from people on the ground. A rich source of raw data is provided by social media, such as Blogs, Twitter or Facebook. In this study we describe two experiments based on the use of Twitter content in the UK, showing that it is possible to detect a flu epidemic, and to assess the levels of rainfall, by analysing text data. These measurements can in turn be used as inputs of more complex systems, for example for the prediction of floods, or disease propagation.
While the question in the title has remained unanswered for thousands of years, it is perhaps easier to address the apparently similar question: "What is intelligence for?"
The strong trend towards the automation of many aspects of scientific enquiry and scholarship has started to affect also the social sciences and even the humanities. Several recent articles have demonstrated the application of pattern analysis techniques to the discovery of non-trivial relations in various datasets that have relevance for social and human sciences, and some have even heralded the advent of "Computational Social Sciences" and "Culturomics".
Submitted by Nello Cristianini on Mon, 21/09/2009 - 16:04
Are We There Yet? Nello Cristianini- University of Bristol [NOTE: this article is currently submitted for publication, and is based on my Keynote Speeches of ICANN 2008 and ECML/PKDD 2009]
Abstract Statistical approaches to Artificial Intelligence are behind most success stories of the field in the past decade. The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things. As we celebrate the spectacular achievements of this line of research, we need to assess its full potential and its limitations. What are the next steps to take towards machine intelligence?
