Flu detector: tracking epidemics on twitter
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Analyzing and predicting viral tweets
Proceedings of the 22nd international conference on World Wide Web companion
Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
How the live web feels about events
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.