Author: Tristan Cann

A new paper Modularity and projection of bipartite networks is now available online.

This paper investigates community detection by modularity maximisation on bipartite networks. In particular we are interested in how the operation of projection, using one node set of the bipartite network to infer connections between nodes in the other set, interacts with community detection. We first define a notion of modularity appropriate for a projected bipartite network and outline an algorithm for maximising it in order to partition the network. Using both real and synthetic networks we compare the communities found by five different algorithms, where each algorithm maximises a different modularity function and sees different aspects of the bipartite structure. Based on these results we suggest a simple ‘rule of thumb’ for finding communities in bipartite networks.

Rudy Arthur won a grant under the AI for Climate Action call from the Turing Institute. Climate change and ecological degradation are already affecting poor and vulnerable people across the world and demand immediate action. This project will apply data science and machine learning techniques to link satellite imaging to rich datasets held by two community-led organisations in rural East Africa; The International Small-Group and Tree Planting Program (TIST) and The Northern Rangelands Trust (NRT). The aim is to understand the socio-ecological mechanisms that promote resilience to climate change and to demonstrate the utility of machine learning and AI for monitoring the UN Sustainable Development Goals (SDGs) and help drive future growth and expansion of TIST.

TIST Program Growth around Mount Kenya from 2005-2019 from TIST Program on Vimeo.

Hywel, Tristan and Kathie all attended the recent European Symposium Series on Societal Challenges focusing on polarisation and radicalisation in Zurich, Switzerland. Each presented their work to the interdisciplinary audience.

Hywel presented his recent work with Iain looking at how politically interested Twitter users engage with hashtags and form tribes around the Brexit debate.

Tristan presented his recent work studying the habits of Twitter users sharing content around the climate change debate and the stark levels of polarisation found among the choice of source by different users.

Kathie presented her recent work exploring the conversation around climate change on Reddit, in particular how different user groups focus on different aspects of the established science or the debate for political intervention.

A new paper “Classification and event identification using word embedding” is now available online.

This paper presents our contribution to the CLEF 2019 Protest-News Track, which aims to classify and identify protest events in English-language news from India and China. We used traditional classification models, namely, support vector machines and XGBoost classifiers, combined with various word embedding approaches. Multiple models were tested for experimental purposes, in addition to the two models evaluated within the official campaign. Results show promising performance, especially in terms of precision on both document and sentence classification tasks.

Our new paper has just been accepted for presentation at CLEF 2019 in September.

Classification and Event Identification Using Word Embedding

This paper presents our contribution to the CLEF 2019 ProtestNews Track, which aims to classify and identify protest events in English-language news from India and China. We used traditional classification models, namely, support vector machines and XGBoost classifiers, combined with various word embedding approaches. Multiple models were tested for experimental purposes, in addition to the two models evaluated within the official campaign. Results show promising performance, especially in terms of precision on both document and sentence classification tasks.

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