Month: December 2018

New paper available online in Sensors.

@choo: Tracking Pollen and Hayfever in the UK Using Social Media

Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to manage their condition and minimise adverse effects. Current pollen forecasts in the UK are based on a sparse network of pollen monitoring stations. Here, we explore the use of “social sensing” (analysis of unsolicited social media content) as an alternative source of pollen and hayfever observations. We use data from the Twitter platform to generate a dynamic spatial map of pollen levels based on user reports of hayfever symptoms. We show that social sensing alone creates a spatiotemporal pollen measurement with remarkable similarity to measurements taken from the established physical pollen monitoring network. This demonstrates that social sensing of pollen can be accurate, relative to current methods, and suggests a variety of future applications of this method to help hayfever sufferers manage their condition.

New paper available online now.

Cann T.J.B., Weaver I.S., Williams H.T.P. (2019) Is it Correct to Project and Detect? Assessing Performance of Community Detection on Unipartite Projections of Bipartite Networks. In: Aiello L., Cherifi C., Cherifi H., Lambiotte R., Lió P., Rocha L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham

Many real-world systems can be represented by bipartite networks that link two classes of node. However, methods for analysing bipartite networks are not as well-developed as those for unipartite networks. In particular, community detection in bipartite networks is often approached by projecting the network onto a unipartite network incorporating just one of the bipartite node classes. Here we apply a simple model to generate bipartite networks with prescribed community structure and then test the performance of community detection using four different unipartite projection schemes. Several important performance issues emerge from this treatment, particularly when the original bipartite networks have a long-tailed degree distribution. We find that a “hyperbolic” projection scheme mitigates performance issues to some extent, but conclude that care must be taken when interpreting community detection algorithm performance on projected networks. These findings have implications for any scenario where a unipartite projection is analysed in place of a bipartite system, including common applications to online social networks, social media and scientific collaboration networks.

Having made a great start to her PhD and become a much liked group member, Sarah Menezes has made a tough decision to go back to industry. A good opportunity came up for her nearby and she has gone for it. We wish her well and she will always be welcome to come back for coffee!

In October, Hywel gave an invited talk at a really interesting workshop on social capital at Amazon HQ in Seattle, entitled “All Boats Rise”. He spoke about political polarisation as viewed through social media, asking whether the increasing focus on understanding social processes using online data is creating a bias towards apparent polarisation. Given that people with moderate or ambivalent views may be less likely to post political content, social media studies are likely to sample the extreme tails of an opinion distribution that is probably normal, with the moderate majority remaining invisible. This is an idea to test some time in 2019. As well as meeting some cool people at the meeting, including academics, social activists and tech entrepreneurs, Hywel also got to tour the Amazon Spheres.