New Paper: Using social media to measure impacts of named storm events in the United Kingdom and Ireland

Michelle’s work on the social sensing of named storm events in the UK and Ireland was recently published in Meteorological Applications:

Using social media to measure impacts of named storm events in the United Kingdom and Ireland

Spruce, M., Arthur, R., Williams, H.T.P.


Despite increasing use of impact‐based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real‐world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm‐related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.


(Top) Time series of the number of tweets per day for named storm events (after filtering for relevance) versus the number of wind tweets per day for the 2017/2018 storm period. Ex‐Hurricane Ophelia produced very high numbers of tweets in the Named Storm and Wind collections for October 16, 2017; that is why plotted counts are truncated for display. Tweet counts for each collection on this date are ~170k (“Ophelia”) and ~60k (“wind”) respectively. (Bottom) Time series of the average UK/Ireland wind speed for the same period. Peaks in wind speed are identified by dashed lines between the two plots to allow visual comparison of wind speed and peaks in wind tweet activity

New funded project: Data Science for Climate Resilience in East Africa

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.

New paper: Utilizing Complex Networks for Event Detection in Heterogeneous High-Volume News Streams.

Iraklis presented his work on network-based event detection using real-time news streams at the recent Complex Networks conference in Lisbon. The paper can be found here:

Moutidis, I. and Williams, H.T.P. (2019) Utilizing Complex Networks for Event Detection in Heterogeneous High-Volume News Streams. Complex Networks and Their Applications VIII, pp 659-672.

Abstract: Detecting important events in high volume news streams is an important task for a variety of purposes. The volume and rate of online news increases the need for automated event detection methods that can operate in real time. In this paper we develop a network-based approach that makes the working assumption that important news events always involve named entities (such as persons, locations and organizations) that are linked in news articles. Our approach uses natural language processing techniques to detect these entities in a stream of news articles and then creates a time-stamped series of networks in which the detected entities are linked by co-occurrence in articles and sentences. In this prototype, weighted node degree is tracked over time and change-point detection used to locate important events. Potential events are characterized and distinguished using community detection on KeyGraphs that relate named entities and informative noun-phrases from related articles. This methodology already produces promising results and will be extended in future to include a wider variety of complex network analysis techniques.

Presentations at European Symposium Series on Societal Challenges 2019

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.

New paper: Classification and event identification using word embedding

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.