Complex network analysis and machine learning for large-scale news-mining
The rise of the web and digital media has seen an explosion in the availability of big social data. This PhD project will apply natural language processing to large collections of text documents harvested from the web (e.g. social media, blogs, online news), in order to construct networks of interaction between people and organisations. These networks will then be analysed to explore how they change in response to external events, with the aim of developing methods to predict real-world events before they occur. For example, one application of such methods might be to derive a dynamic network of relationships between politicians based on online news articles – could this network be used to reveal patterns of power and influence, identify new political players, or to predict election outcomes?
The student will learn natural language processing and network analysis, with aspects of machine learning for identification of influential actors. The fully funded 4-year project is co-sponsored by University of Exeter and a fast-growing data science company with offices in London and Bristol. The studentship will be based in the Computer Science department at University of Exeter and will interact with a vibrant interdisciplinary data science research community centred on the new Data Science Institute. The project offers excellent prospects for training and career progression.
Candidates should have a strong background in a quantitative discipline (e.g. maths, physics, computer science), with programming skills and experience of data analysis. Interested candidates are encouraged to contact Dr Hywel Williams (firstname.lastname@example.org) for further information.
|Lab||University of Exeter|
|Keywords||complex networks; machine learning; computational social science; data science; statistics; simulation; social media;|
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