Wednesday, 15 August 2018

What matters most to people around the world? Using the GATE social media toolkit to investigate wellbeing.

As part of the EU SoBigData project, the GATE team hosts a number of short research visits, between 2 weeks and 2 months, for all kinds of data scientists (PhD students, researchers,  academics, professionals) to come and work with us and use our tools and/or datasets on a project involving text mining and social media analysis. One such visitor was Economics PhD student Giuliano Resce from the University of Roma Tre in Italy. During his month-long visit, he worked with Diana Maynard on a project collecting and analysing millions of public tweets in 7 different languages, in order to understand the different societal priorities of people in different countries of the OECD. The work explored the different opinions on Twitter of people around the world about societal issues such as the environment, housing and life satisfaction.


OECD Better Life Index


Giuliano first used the GATE Twitter Collector to collect a set of tweets, and then processed them with the GATE social media analysis toolkit, using GATE Mimir to investigate the results. Topics were determined using the initial set of OECD topics, in 7 languages, which we then expanded for each language into a set of keywords for each topic using first existing lists from the GATE political tweets analyser and then Word2Vec to find more related keywords to those.

Better Life Index Topic frequency at county level in Twitter (percentage)

The ensuing analysis of the tweets then enabled Giuliano to redesign Composite Indices for the OECD’s Better Life Index, a measure of well-being which gives a detailed overview of the social, economic and environmental performances of different countries. In turn, this redesign helps to better reflect the actual needs of the people. The idea is that the aggregate of millions of tweets may provide a representation of the different priorities among the eleven topics of the Better Life Index. By combining topic performances and related Twitter trends, they produced new evidence about the relationship between people’s priorities and policy makers’ activity in the BLI framework.


Rank in Composite BLI using local Twitter trends as Weights and using Equal Weights

A paper about the work has been published in the Journal of Technological Forecasting & Social Change.


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