Summer school trip: Lake Como School of Complex Networks

Iraklis and I recently attended the Lake Como School of Advanced Studies summer school on Complex Networks: Theory, Methods and Applications.

The school featured a number of talks from prominent scholars in the field of complex networks. These sessions included both theoretical backgrounds and methodologies, as well as a number of experimental examples and real world applications of network science. During the Wednesday afternoon session, many students, including myself, presented on their recent work. This provided a really valuable opportunity to share new insights into the cutting-edge research taking place in universities across the world.

Beyond the academic benefit of a week’s study, we got to spend the week enjoying the weather of a Mediterranean spring whilst exploring the beautiful town of Como, situated in the foothills of the Alps.

Lake view.
The view from the lecture theatre at Lake Como.

Thanks to the speakers to the speakers for their interesting sessions, the other students for their excellent company and our funders for covering the costs. I really enjoyed my time at the school and look forward to putting some of the ideas I picked up to good use in the future.

New publication: Social sensing of floods in the UK

“Social sensing” is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes ‘relevance’ filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.

Floodiness grid, 64 × 64, over England and Wales on 5/12/2015 using (r, α, T) = (1.0, 0.15, 0.1).
Using tweets collected in 1 hour windows. White indicates no tweets. Colour bar units are floodiness relative to daily max. Top left: 10am-11am. Top Right: 1pm-2pm. Bottom Left: 4pm-5pm. Bottom Right: 9pm-10pm.

Read this article online.

New publication: Dynamic social media affiliations among UK politicians

Inter-personal affiliations and coalitions are an important part of politicians’ behaviour, but are often difficult to observe. Since an increasing amount of political communication now occurs online, data from online interactions may offer a new toolkit to study ties between politicians; however, the methods by which robust insights can be derived from online data require further development, especially around the dynamics of political social networks. We develop a novel method for tracking the evolution of community structures, referred to as ‘multiplex community affiliation clustering’ (MCAC), and use it to study the online social networks of Members of Parliament (MPs) and Members of the European Parliament (MEPs) in the United Kingdom. Social interaction networks are derived from social media (Twitter) communication over an eventful 17-month period spanning the UK General Election in 2015 and the UK Referendum on membership of the European Union in 2016. We find that the social network structure linking MPs and MEPs evolves over time, with distinct communities forming and re-forming, driven by party affiliations and political events. Without including any information about time in our model, we nevertheless find that the evolving social network structure shows multiple persistent and recurring states of affiliation between politicians, which align with content states derived from topic analysis of tweet text. These findings show that the dominant state of partisan segregation can be challenged by major political events, ideology, and intra-party tension that transcend party affiliations.

Political retweet communities
An example network based on retweet interactions (see Methods) over the 7-day period from 2016-05-30 to 2016-06-06. Nodes represent MPs/MEPs and node properties indicate party affiliation (colour), stated position on UK membership of the EU (closed – remain, open – leave), and their status as an MP (circle) or MEP (triangle).

Read the article online.