What can Flickr photographs tell us about the world?

In their second year, our undergraduate web scientists undertake a group project module (WEBS2002, led by Jonathon Hare & co-taught by Su White) in which they get to apply what they learnt in the first year to a practical web-science problem, and also learn about team-working. For the project this semester, the students were provided with a large dataset of geolocated images and associated metadata collected from the Flickr website. Using this data, they were tasked with exploring what this data could tell us about the world. In this seminar the two groups will present the outcomes of their work. Team Alpha (Ellie Hamilton, Clayton Jones & Alok Acharya) will present their work on "The relationship between Group Photos, Social Integration and Suicide". This work aims to explore whether levels of social integration (which Durkheim posited as a factor in "Egoistic Suicide" rates) can be predicted by measuring the proportion of photos of groups of people to photos of individuals within a geographical region. Team Bravo (Agnieszka Grzesiuk-Szolucha, Thomas Leese & Ammaar Tawil) will present their work on "Sentiment Analysis on Flickr Photo Tags to Classify a Photo as Positive or Negative, In Order to Determine the Happiness of a Country or Region". This work explores whether estimates of sentiment made by applying SentiWordNet to Flickr tags correlate with indices of world happiness and socio-economic well-being.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.