AIM CISC Project wins Digital Research award

The award was presented by the University of Edinburgh's Centre for Data, Culture and Society last week.

The team of Eleojo Abubukar, Andreas Grivas, Claire Grover, Richard Tobin, Clare Llewellyn, Chunyu Zheng, Chris Dibben, Alan MArshall, Jamie Pearce and Beatrice Alex,  won the award for their project looking at local news as a means of determining local health: 'News media as a lens: exploring neighbourhood health through the analysis of geoparsed and clustered local news.'

In making their decision, the judges said:

The understanding of the multifaceted impact of communities on health remains challenging to define, with limited insight into place-based processes and their implications for health and inequalities. This knowledge gap undermines the development of effective policy interventions aimed at enhancing local health and well-being. This project investigated the innovative use of computational news analysis to complement statistical information collected to analyse neighbourhood health. This project explored whether it is possible to capture aspects of social dimensions and community of place using themes in local newspapers which had previously not been leveraged in place-based health research relying only on statistical information in databases like the Scottish Index of Multiple Deprivation (SIMD).

The project applied established NLP techniques, such as geoparsing via the Edinburgh Geoparser combined with topic modelling using hierarchical clustering, to over 70K news articles reporting on areas in the City of Edinburgh in this novel research setting.  The NLP output (news topic distribution of areas) feeds into a regression analysis related to health resilience and vulnerability to explore if computational analysis of local newspaper text can capture social aspects of place that are neglected in SIMD and other area-based measures.  To ensure that the news clustering is accurate, researchers performed cluster evaluation and showed that it performs well.  As a check, they also showed that news topics, which are already well captured in statistical data, such as ones related to crime or property, correlate as expected with deprivation statistics. More crime is reported for areas with higher deprivation and reporting on the property market increases for areas of lower deprivation. Most notably, a regression-based analysis showed that resilient areas are associated with increases in local press on topics like heritage, community sports (boxing) and managed crime, whereas vulnerable areas are associated with increased local press on topics of littering and anti-social problems, unsolved crime and local politics. These results show that combining NLP of news with statistical analysis can provide insights on local neighbourhood characteristics which can assist in explaining their resilience.

To find out more about the awards, click here.

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