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.Comment: 24 pages, 6 figure

FALCON: a software package for analysis of nestedness in bipartite networks

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts

Sponsored messaging about climate change on Facebook: Actors, content, frames

Online communication about climate change is central to public discourse around this contested issue. Facebook is a dominant social media platform known to be a major source of information and online influence, yet discussion of climate change on the platform has remained largely unstudied due to difficulties in accessing data. This paper utilises Facebook's repository of social/political ads to study how climate change is framed as an issue in adverts placed by different actors. Sponsored content is a strategic investment and presumably intended to be persuasive, so patterns of who pays for adverts and how those adverts frame the issue can reveal large-scale trends in public discourse. We show that most money spent on climate-related messaging is targeted at users in the US, GB and CA. While the number of advert impressions correlates with total spend by an actor, there is a secondary effect of unpaid social sharing which can substantially affect the number of impressions per dollar spent. Most spend in the US is by political actors, while environmental non-governmental organisations dominate spend in GB. Analysis shows that climate change solutions are well represented in GB, while climate change impacts such as extreme weather events are strongly represented in the US and CA. Different actor types frame the issue of climate change in different ways; political actors position the issue as party political and a point of difference between candidates, whereas environmental NGOs frame climate change as the focus of collective action and social mobilisation. Overall, our study provides a first empirical exploration of climate-related advertising on Facebook. It shows the diversity of actors seeking to use Facebook as a platform for their campaigns and how they utilise different topic frames to persuade users to act.Comment: 44 pages, 9 figure

Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals a heavy-tailed distribution of response in online discourse to strategic communications.Comment: 12 pages, 5 figure

Virtual learning environment engagement and learning outcomes at a 'bricks-and-mortar' university

In this study, we analyse the relationship between engagement in a virtual learning environment (VLE) and module grades at a ‘bricks-and-mortar’ university in the United Kingdom. We measure VLE activity for students enrolled in 38 different credit-bearing modules, each of which are compulsory components of six degree programmes. Overall we find that high VLE activity is associated with high grades, but low activity does not necessarily imply low grades. Analysis of individual modules shows a wide range of relationships between the two quantities. Grouping module-level relationships by programme suggests that science-based subjects have a higher dependency on VLE activity. Considering learning design (LD), we find that VLE usage is more important in modules that adopt an instruction-based learning style. We also test the predictive power of VLE usage in determining grades, again finding variation between degree programmes and potential for predicting a student's final grade weeks in advance of assessment. Our findings suggest that student engagement with learning at a bricks-and-mortar university is in general hard to determine by VLE usage alone, due to the predominance of other “offline” learning activities, but that VLE usage can nonetheless help to predict performance for some disciplines

Student engagement and wellbeing over time at a higher education institution

Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students' subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records

The value of the pragmatic-explanatory continuum indicator summary wheel in an ongoing study: the bullous pemphigoid steroids and tetracyclines study

BACKGROUND: The Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) tool is intended to be used in the design phase of trials to help investigative teams design trials in-line with their purpose. Our team applied this tool to an ongoing trial (BLISTER) to determine whether the initial suggestion among some team members that the trial could be described as largely pragmatic was the consensus. METHODS: Each of the six members of the BLISTER trial team was sent a blank PRECIS wheel to independently complete. The results obtained were averaged and plotted on a single PRECIS wheel to illustrate the degree of pragmatism of the trial. RESULTS: The trial team found that the design of the trial was closest to the pragmatic end of the pragmatic-explanatory continuum. The strongest consensus was found on the 'flexibility of the comparison intervention' and 'practitioner adherence' domains (SD = 13). The trial team appeared to disagree most on the 'eligibility criteria' (SD = 35) and 'participant compliance' (SD = 31) domains, although the large standard deviations were a result of a single outlier in the two domains. CONCLUSION: The PRECIS tool can be used to retrospectively determine the pragmatism of a trial provided enough expertise and information on the trial is available. Illustrating the design of a trial on the PRECIS wheel can help research users more easily identify studies of interest. We hope our recommendations for applying this useful tool will encourage others to consider using it when designing, conducting and reporting studies. TRIAL REGISTRATION: Current Controlled Trials http://www.controlled-trials.com/ISRCTN13704604

Daisyworld: a review

Daisyworld is a simple planetary model designed to show the long-term effects of coupling between life and its environment. Its original form was introduced by James Lovelock as a defense against criticism that his Gaia theory of the Earth as a self-regulating homeostatic system requires teleological control rather than being an emergent property. The central premise, that living organisms can have major effects on the climate system, is no longer controversial. The Daisyworld model has attracted considerable interest from the scientific community and has now established itself as a model independent of, but still related to, the Gaia theory. Used widely as both a teaching tool and as a basis for more complex studies of feedback systems, it has also become an important paradigm for the understanding of the role of biotic components when modeling the Earth system. This paper collects the accumulated knowledge from the study of Daisyworld and provides the reader with a concise account of its important properties. We emphasize the increasing amount of exact analytic work on Daisyworld and are able to bring together and summarize these results from different systems for the first time. We conclude by suggesting what a more general model of life-environment interaction should be based on

Mutations in TOP3A Cause a Bloom Syndrome-like Disorder

Bloom syndrome, caused by biallelic mutations in BLM, is characterized by prenatal-onset growth deficiency, short stature, an erythematous photosensitive malar rash, and increased cancer predisposition. Diagnostically, a hallmark feature is the presence of increased sister chromatid exchanges (SCEs) on cytogenetic testing. Here, we describe biallelic mutations in TOP3A in ten individuals with prenatal-onset growth restriction and microcephaly. TOP3A encodes topoisomerase III alpha (TopIIIα), which binds to BLM as part of the BTRR complex, and promotes dissolution of double Holliday junctions arising during homologous recombination. We also identify a homozygous truncating variant in RMI1, which encodes another component of the BTRR complex, in two individuals with microcephalic dwarfism. The TOP3A mutations substantially reduce cellular levels of TopIIIα, and consequently subjects’ cells demonstrate elevated rates of SCE. Unresolved DNA recombination and/or replication intermediates persist into mitosis, leading to chromosome segregation defects and genome instability that most likely explain the growth restriction seen in these subjects and in Bloom syndrome. Clinical features of mitochondrial dysfunction are evident in several individuals with biallelic TOP3A mutations, consistent with the recently reported additional function of TopIIIα in mitochondrial DNA decatenation. In summary, our findings establish TOP3A mutations as an additional cause of prenatal-onset short stature with increased cytogenetic SCEs and implicate the decatenation activity of the BTRR complex in their pathogenesis