PhD Studentship

PhD Studentship in Improving News Reader Experience with Online Journalism

Application deadline: 17 March 2017 - Studentship suitable for EU/UK students only.

We seek strong candidates for a 4-year, joint Masters and PhD research studentship in the EPSRC Centre for Doctoral Training (CDT) in Data Science at the University of Edinburgh, on the topic Improving News Reader Experience with Online Journalism.  This studentship is co-funded by Financial Times and will be jointly supervised by Dr. Walid Magdy and Financial Times.

The research project focuses on building technology for improving news reader experience, which includes:

  • Automatically enriching news articles with metadata, such as topics, summaries, and highlights.
  • Detecting the main features in an article/news that makes it popular.
  • Finding background and features of readers who get interested in specific subjects.
  • Building targeted recommender systems to users based on content and users’ features.

Candidates ideally should have good background in one or more of the following: text mining, natural language processing, and social media analysis. They also should hold good honours degree in a related subject and have high quality programming skills.

Prospective student would be encouraged to spend some time during PhD at Financial Times to work closely in a practical environment, and potentially implement some of the developed technology at their end.

Studentship suitable for EU/UK students only.

 

Project Background

Financial Times (FT) produces a lot of journalism each day with a global network of 600 journalists. Much of that has a limited shelf life because news stories move on incredibly quickly. A limited proportion of this journalism can be only promoted via editorially curated spaces (FT website/social media accounts).  In the age of classical printed newspaper, it was much easier for readers to discover the full breadth of journalism, where serendipity is more natural. Digitally, people tend to be more directed in their search for information.  FT goal is to better understand and predict reader interests, and successfully match these to pieces of content, which can help readers find the right content at the right time.  Distilling the essence of an article is key.  The sooner new topics emerging can be identified, the more likely FT would be able to capitalise on the topic.

Recently, FT launched a web site that uses metadata at the core to drive navigation and discovery of content. Customers can opt to follow particular 'topics', which might be theme or entity based, and receive alerts and personalised curations using this metadata.  Improvements that could be made here have a direct benefit in which the readers get more utility and value and FT gets greater value from the investment that was made in creating the piece of journalism.

FT is interested in studying the bottom up methods of classifying content and supporting relevant topic discovery, using FT journalism and news content in general, to identify emerging topics.  The general objective is supporting readers in filtering information, whether through creating richer metadata, summaries and usage data to allow readers to explore relevant stories, or exploiting knowledge graph/linked data to synthesise and sequence topics of interest.

 

About the ESPRC CDT in Data Science

The CDT focuses on the computational principles, methods and systems for extracting knowledge from data. Large data sets are now generated by almost every activity in science, society and commerce - ranging from molecular biology to social media, from sustainable energy to health care.  Data science asks: How can we efficiently find patterns in these vast streams of data?  Many research areas have tackled parts of this problem:

  • machine learning focuses on finding patterns and making predictions from data;
  • databases are needed for efficiently accessing data and ensuring its quality;
  • ideas from algorithms are required to build systems that scale to big data streams;
  • the mathematical fields of statistics and optimization provide foundational tools and theory;
  • natural language processing, computer vision and speech processing consider the analysis of different types of unstructured data.

Recently, these distinct disciplines have begun to converge into a single field called Data Science.

The CDT is a 4-year programme: the first year provides Masters level training in the core areas of Data Science, along with a significant project. In years 2-4 students will carry out PhD research in Data Science, guided by PhD supervisors from within the Centre.  The CDT is funded by EPSRC and the University of Edinburgh.

Edinburgh has a large, world-class research community in Data Science to support the work of the CDT student cohort.  The city of Edinburgh has often been voted the 'best place to live in Britain' and has many exciting cultural and student activities.

Apply now

Prospective applicants are encouraged to contact Walid Magdy to discuss the studentship before submitting an application.  Studentship suitable for EU/UK students only.

Applications must be received by 17 March 2017. Please apply through the CDT in Data Science application page.