Using social media to measure impacts of named storm events in the United Kingdom and Ireland

This is the final version. Available on open access from Wiley via the DOI in this recordDespite increasing use of impact-based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real-world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm-related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.Economic and Social Research Council (ESRC)Engineering and Physical Sciences Research Council (EPSRC)Natural Environment Research Council (NERC

Different news for different views: Political news-sharing communities on social media through the UK General Election in 2015

This is the final version of the article. Available from AAAI via the URL in this record.Media exposure is a central concept in understanding the dynamics of public opinion and political change. Traditional models of media exposure have been severely challenged by the shift to online news consumption and news-sharing on social media. Here we use network analysis and automated content analysis to examine the interaction between news media and social media around the UK General Election in 2015. We study a large corpus of UK newspaper articles and Twitter content, finding significant temporal correlations between newspaper topic coverage and the content discussed on Twitter. We also identify news-sharing communities around groups of news sources that are ideologically clustered. Analysis of topics covered within each group shows that different communities are exposed to different news content during the election. Our results confirm that ideological bias and selective news-sharing affect patterns of online media exposure in social media.This work was supported by the UK Economic and Social Research Council ES/N012283/1

Using social media to detect and locate wildfires

This is the final version of the article. Available from AAAI via the URL in this record.Methods for detecting and tracking natural hazards continue to increase in coverage, resolution and reliability. However, information on the social impacts of natural hazards is often lacking. Here we test the feasibility of using social media data (Twitter and Instagram) to detect and map an important class of natural hazard: wildfires. We analyse social media posts associated with wildfires over several time periods and compare them with wildfire occurrence data derived from satellite-based remote sensing data and on-the-ground observations. For the whole of the contiguous United States, we find significant temporal correlations between wildfire-related social media activity and wildfire occurrence, but also that there is substantial variation in the strength of this relationship at smaller spatial scales (states and counties). We then explore the utility of social media for location of wildfire events, finding good evidence to support further development of such methods. We conclude by discussing several challenges and opportunities for application of this novel data resource to provide information on impacts of natural hazards.The authors were supported by a Research on Changes of Variability and Environmental Risk (RECoVER) grant funded by EPSRC (EP/M008495/1)

Online misinformation about climate change

This is the final version. Available from the publisher via the DOI in this record.Policymakers, scholars, and practitioners have all called attention to the issue of misinformation in the climate change debate. But what is climate change misinformation, who is involved, how does it spread, why does it matter, and what can be done about it? Climate change misinformation is closely linked to climate change skepticism, denial, and contrarianism. A network of actors are involved in financing, producing, and amplifying misinformation. Once in the public domain, characteristics of online social networks, such as homophily, polarization, and echo chambers—characteristics also found in climate change debate—provide fertile ground for misinformation to spread. Underlying belief systems and social norms, as well as psychological heuristics such as confirmation bias, are further factors which contribute to the spread of misinformation. A variety of ways to understand and address misinformation, from a diversity of disciplines, are discussed. These include educational, technological, regulatory, and psychological-based approaches. No single approach addresses all concerns about misinformation, and all have limitations, necessitating an interdisciplinary approach to tackle this multifaceted issue. Key research gaps include understanding the diffusion of climate change misinformation on social media, and examining whether misinformation extends to climate alarmism, as well as climate denial. This article explores the concepts of misinformation and disinformation and defines disinformation to be a subset of misinformation. A diversity of disciplinary and interdisciplinary literature is reviewed to fully interrogate the concept of misinformation—and within this, disinformation—particularly as it pertains to climate change. This article is categorized under:. Perceptions, Behavior, and Communication of Climate Change > Communication.Economic and Social Research Council (ESRC

Social sensing of floods in the UK

This is the final version of the article. Available from Public Library of Science (PLoS) via the DOI in this record.“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

Alternative mechanisms for Gaia

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordA long-standing objection to the Gaia hypothesis has been a perceived lack of plausible mechanisms by which life on Earth could come to regulate its abiotic environment. A null hypothesis is survival by pure chance, by which any appearance of regulation on Earth is illusory and the persistence of life simply reflects the weak anthropic principle - it must have occurred for intelligent observers to ask the question. Recent work has proposed that persistence alone increases the chance that a biosphere will acquire further persistence-enhancing properties. Here we use a simple quantitative model to show that such ‘selection by survival alone’ can indeed increase the probability that a biosphere will persist in the future, relative to a baseline of pure chance. Adding environmental feedback to this model shows either an increased or decreased survival probability depending on the initial conditions. Feedback can hinder early life becoming established if initial conditions are poor, but feedback can also prevent systems from diverging too far from optimum environmental conditions and thus increase survival rates. The outstanding question remains the relative importance of each mechanism for the historical and continued persistence of life on Earth.Gaia CharityUniversity of Exete

Multiple states of environmental regulation in well-mixed modle biospheres.

The Gaia hypothesis postulates that life influences Earth’s feedback mechanisms to form a self-regulating system. This provokes the question: how can global self-regulation evolve? Most models demonstrating environmental regulation involving life have relied on alignment between local selection and global regulation. In these models environment-improving individuals or communities spread to outcompete environment degrading individuals / communities, leading to global regulation, but this depends on local differences in environmental conditions. In contrast, well-mixed components of the Earth system, such as the atmosphere, lack local environmental differentiation. These previous models do not explain how global regulation can emerge in a system with no well-defined local environment, or where the local environment is overwhelmed by global effects. We present a model of self-regulation by ‘microbes’ in an environment with no spatial structure. These microbes affect an abiotic ‘temperature’ as a byproduct of metabolism. We demonstrate that global self-regulation can arise in the absence of spatial structure in a diverse ecosystem without localised environmental effects. We find that systems can exhibit nutrient limitation and two temperature limitation regimes where the temperature is maintained at a near constant value. During temperature regulation, the total temperature change caused by the microbes is kept near constant by the total population expanding or contracting to absorb the impacts of new mutants on the average affect on the temperature per microbe. Dramatic shifts between low temperature regulation and high temperature regulation can occur when a mutant arises that causes the sign of the temperature effect to change. This result implies that self-regulating feedback loops can arise without the need for spatial structure, weakening criticisms of the Gaia hypothesis that state that with just one Earth, global regulation has no mechanism for developing because natural selection requires selection between multiple entitie

Gaian bottlenecks and planetary habitability maintained by evolving model biospheres: The ExoGaia model

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The search for habitable exoplanets inspires the question - how do habitable planets form? Planet habitability models traditionally focus on abiotic processes and neglect a biotic response to changing conditions on an inhabited planet. The Gaia hypothesis postulates that life influences the Earth's feedback mechanisms to form a self-regulating system, and hence that life can maintain habitable conditions on its host planet. If life has a strong influence, it will have a role in determining a planet's habitability over time. We present the ExoGaia model - a model of simple 'planets' host to evolving microbial biospheres. Microbes interact with their host planet via consumption and excretion of atmospheric chemicals. Model planets orbit a 'star' which provides incoming radiation, and atmospheric chemicals have either an albedo, or a heat-trapping property. Planetary temperatures can therefore be altered by microbes via their metabolisms. We seed multiple model planets with life while their atmospheres are still forming and find that the microbial biospheres are, under suitable conditions, generally able to prevent the host planets from reaching inhospitable temperatures, as would happen on a lifeless planet. We find that the underlying geochemistry plays a strong role in determining long-term habitability prospects of a planet. We find five distinct classes of model planets, including clear examples of 'Gaian bottlenecks' - a phenomenon whereby life either rapidly goes extinct leaving an inhospitable planet, or survives indefinitely maintaining planetary habitability. These results suggest that life might play a crucial role in determining the long-term habitability of planets.We thank the Gaia Charity and the University of Exeter for their support of this work

Good and bad events: Combining network-based event detection with sentiment analysis

This is the final version. Available on open access from Springer via the DOI in this recordThe 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.Adarga Ltd.Turing InstituteUniversity of Exete