New paper: Communities of online news exposure during the UK General Election 2015

New paper available in Online Social Networks and Media

Communities of online news exposure during the UK General Election 2015

Media exposure has become increasingly complex and hard to measure with the rise in online news consumption. Furthermore, since many people now routinely access news via social media, questions arise as to whether social news-sharing is affected by the polarization and partisan echo chambers that are often observed in social media communication. This study considers news-sharing on Twitter during the UK General Election in 2015, using the act of sharing as an indicator that the sharer has been exposed to that online news content. Analysis of the network structure of users and the news articles they share identifies multiple distinct user communities, which are characterized by analysis of the articles shared within them. Communities are characterised by news article sources (web domains), geographical origin and content; time of article publication was also considered but showed no significant relationships. There is evidence for ideologically biased audiences that predominantly share content from either left-leaning or right-leaning news sources, but these audiences also see content from opposing viewpoints. Other audiences are characterized by geography and/or specialised on particular news topics. Overall these findings suggest that many people consume a diverse range of news content over the election period and that the level of political bias in content exposure varies widely across the Twitter user population.

New paper: Scaling Laws in Geo-located Twitter Data

New paper accepted for publication in PLOS One

Scaling Laws in Geo-located Twitter Data

We observe and report on a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power laws, with exponents greater than one, that are consistent with each other and across a range of spatial scales. This implies that population density can accurately predict Twitter activity. Furthermore this trend can be used to identify ‘anomalous’ areas that deviate from the trend. Analysis of geo-tagged and place-tagged tweets show that geo-tagged tweets are different with respect to user type and content. Our findings have implications for the spatial analysis of Twitter data and for understanding demographic biases in the Twitter user base.

Presentations for XCS group

Recently, Michelle and Tristan had the opportunity to present our work tying together social media, the weather and climate change to the XCS group here in the University. It was a great chance to connect with other researchers and share their work with new audiences.

Michelle presented her work looking at the use of Twitter data to ‘nowcast’, that is looking at how tweets can be used to augment traditional sensors to measure the impacts of storms, floods and other weather events.

Tristan shared his work studying how people share information about climate change on Twitter, in particular how polarisation in interactions extends to media choices.

Presentation at Complex Networks 2018 – Cambridge, UK

Shortly before Christmas, I attended Complex Networks 2018 in Cambridge. It was a great opportunity to meet other scholars in the field of network science and hear about the latest research from around the world.

This conference published our recent paper studying projections of bipartite networks, which I presented to the other attendees. Many valuable questions and discussions followed the presentation and I’m grateful to all those that were interested for their insightful comments.

Many thanks to the organisers for an excellent conference.

New post-doctoral research fellow: Anais Ollagnier

I started this year as a research fellow supervised by Hywel Williams. I am working on the development of the Institute of Coding Career Coach. I am investigating how we can manage social data in order to leverage them efficiently. My topics of interest are machine learning and natural language processing (NLP).

Previously, I completed my PhD in Computer Science at the University of Aix-Marseille. Throughout this, I focused on information retrieval and NLP, in the context of a digital scientific library. I designed algorithms in order to exploit the possibilities of digital access to scientific information.