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"Williams, Matthew"
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Cardiff Castle and the Marquesses of Bute
Cardiff Castle is a major Roman, Norman and medieval survival, but what sets it apart is its extraordinary redevelopment during the 18th and 19th centuries, culminating in the extraordinary fairytale gothic revival extravagances we see today. In this sumptuous illustrated study of the past 250 years of its history, the Castle's curator celebrates this reinvention that was led by several generations of the wealthy Bute family. 18th-century building and landscape work by the renowned landscape designer \"Capability\" Brown and the architect Henry Holland was followed by William Burges' fantastical transformations in the 19th century, together creating what is now one of the most iconic and popular buildings in Wales. Architectural historian Matthew Williams has been the Historian and Curator of Cardiff Castle since 1990.
Book
Towards an Ethical Framework for Publishing Twitter Data in Social Research
New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent. While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications. To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.
Journal Article
A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
The Koopman operator is a
linear
but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear dynamical systems. In this manuscript, we present a data-driven method for approximating the leading
eigenvalues, eigenfunctions, and modes
of the Koopman operator. The method requires a data set of snapshot pairs and a dictionary of scalar observables, but does not require explicit governing equations or interaction with a “black box” integrator. We will show that this approach is, in effect, an extension of dynamic mode decomposition (DMD), which has been used to approximate the Koopman eigenvalues and modes. Furthermore, if the data provided to the method are generated by a Markov process instead of a deterministic dynamical system, the algorithm approximates the eigenfunctions of the Kolmogorov backward equation, which could be considered as the “stochastic Koopman operator” (Mezic in Nonlinear Dynamics 41(1–3): 309–325,
2005
). Finally, four illustrative examples are presented: two that highlight the quantitative performance of the method when presented with either deterministic or stochastic data and two that show potential applications of the Koopman eigenfunctions.
Journal Article
Gardenista : the definitive guide to stylish outdoor spaces
\"The book contains lushly photographed tours of 12 enviable gardens; planting guides for a variety of climates and color palettes; in-depth case studies on more than a dozen outdoor structures (from yoga studios to chicken coops); do-it-yourself projects; easy-to-implement design ideas; \"The Gardenista 100,\" a guide to timeless everyday objects for the outdoors; plus advice from landscape professionals. Equal parts inspiration and expert intel, Gardenista is both a perfect starting point and an all-in-one manual when questions arise\"--Amazon.com.
Book
Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime
Abstract
National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age.
Journal Article
GUARDIANS UPON HIGH: AN APPLICATION OF ROUTINE ACTIVITIES THEORY TO ONLINE IDENTITY THEFT IN EUROPE AT THE COUNTRY AND INDIVIDUAL LEVEL
Online fraud is the most prevalent acquisitive crime in Europe. This study applies routine activities theory to a subset of online fraud, online identity theft, by exploring country-level mechanisms, in addition to individual determinants via a multi-level analysis of Eurobarometer survey data. This paper adds to the theory of cybercrime and policy debates by: (1) showing that country physical guardianship (e. g. cyber security strategy) moderates the effects of individual physical guardianship; (2) introducing a typology of online capable guardianship: passive physical, active personal and avoidance personal guardianship; (3) showing that online identity theft is associated with personal and physical guardianship; and (4) identifying public Internet access and online auction selling as highly risky routine activities. The paper concludes by emphasizing the importance of studying country-level effects on online identity theft victimization.
Journal Article
Mudlark'd. : hidden histories from the River Thames
\"Mudlark'd combines insights from two hundred rare objects discovered on the foreshore of the River Thames with a wealth of breathtaking illustrations to uncover the hidden histories of ordinary people from prehistory to today. Malcolm Russell tells the stories behind each find, revealing the habits, customs, and artistry of the people who created and used it. In the eighteenth and nineteenth centuries, London was the busiest port in the world, exchanging goods and ideas with people from every continent. The shores of the Thames have long been densely packed with taverns, brothels, and markets, and the river's muddy banks are a repository of intriguing and precious objects that evoke long-forgotten ways of life. With Russell as your guide, a bottleneck of a jug is shown to be a talisman to counter the ill effects of witchcraft. Glass beads expose the brutal realities of the transatlantic slave trade. Clay tobacco pipes uncover the lives of Victorian magicians. A scrap of Tudor cloth illuminates the experiences of Dutch and French religious refugees. These are just some of the stories told in Mudlark'd, which also contains a primer, giving advice on how to mudlark on tidal rivers around the world and outlining the tools and equipment you will need.\"--Publisher's website.
Book
Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data
This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect \"signatures\" of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.
Journal Article