New paper: Ideological biases in social sharing of online information about climate change

Tristan, Iain and Hywel have recently published a paper exploring the information sharing ecosystem of climate change on Twitter. This work is available now in open access at PLOS ONE.

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

New paper: pyveg: A Python package for analysing the time evolution of patterned vegetation using Google Earth Engine

Josh and Chris have recently published some work with collaborators from the Alan Turing Institute in the Journal of Open Source Software. This paper is pyveg: A Python package for analysing the time evolution of patterned vegetation using Google Earth Engine and is available online.

Periodic vegetation patters occur throughout the world in water limited systems. These patterns take the form of spots, labyrinths and gaps and undergo morphometric changes as they experience long term reductions in rainfall. This provides a useful indicator of the health of drylands. Previous efforts to evaluate these patterns have been qualitative or if quantitative, have been sporadic and have examined small scale sites. These pattern vegetation sites often occur in hard to reach locations. We developed a new way of quantitatively evaluating the state of patterned vegetation and assign a numerical value to their morphology. When applied to satellite data, this enables a long term analysis of pattern vegetation health, as well as their resilience. We have developed a python package, pyveg, which encapsulates this methodology and enables the user to perform long term pattern vegetation analysis of any site in the world.

New work on an Air Quality Monitoring system

Dr. Rudy Arthur has been working together with the Haywards Heath Town Council and Haywards Heath Green Party on an Air Quality Monitoring system. Thanks to an award from Exeter’s Strategic Priorities Fund we were able to construct an innovative solar powered, semi-permanent pollution monitoring installation that provides near real time updates on Air Quality. Read more on the Haywards Heath Green Party website.

New paper: Good and bad events: combining network-based event detection with sentiment analysis

Iraklis and Hywel have recently published a new paper Good and bad events: combining network-based event detection with sentiment analysis in Social Network Analysis and Mining.

The huge volume and velocity of media content published on the Web presents a substantial challenge to human analysts. In prior work, we developed a system (network event detection, NED) to assist analysts by detecting events within high-volume news streams in real time. NED can process a heterogeneous stream of news articles or social media user posts, combining text mining and network analysis to detect breaking news stories and generate an easy-to-understand event summary. In this paper, we expand the NED event detection and summarisation approach in two ways. First, we introduce a new approach to named entity disambiguation for tweets, which contain minimal information due to brevity. Second, we apply sentiment analysis techniques to documents associated with a detected event to characterise the event as either broadly ‘positive’ or ‘negative’ based on media portrayal. Our expansion focuses on Twitter streams since Twitter has become an important news dissemination platform and is often the site where emerging events are first seen. To test the extended methodology, we apply it here to three data sets related to political elections in the UK and the USA. The addition of sentiment analysis to the NED event detection methodology improves the insight gained by the user by allowing quick evaluation of the perceived impact of an event. This approach may have potential applications in domains where public sentiment is relevant to decision-making around events, such as financial markets and politics.