Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Global cities, creative industries and their representation on social media: a micro-data analysis of twitter data on the fashion industry

The creative and cultural industries form an important part of many urban economies, and the fashion industries are one of the exemplar creative industries. Because fashion is based on intangibles such as branding and reputation, it tends to have a two-way relationship with cities: urban areas market themselves through their fashion industry, while the fashion industry draws heavily on the representation of place. In this paper we investigate this interlinked relationship between the fashion industry and place in four of the major cities of global fashion – London, New York, Milan and Paris – using data from the social media platform Twitter. To do this, we draw upon a variety of computer-aided text analysis techniques – including cluster, correspondence and specificity analyses – to examine almost 100,000 tweets collected during the Spring–Summer fashion weeks of February and March 2018. We find considerable diversity in how these cities are represented. Milan and Paris are seen in terms of national fashion houses, artisanal production and traditional institutions such as galleries and exhibitions. New York is focused on media and entertainment, independent designers and a ‘buzzy’ social life. London is portrayed in the most diverse ways, with events, shopping, education, social movements, political issues and the royal family all prominent. In each case, the historical legacy and built environment form important parts of the city’s image. However, there is considerable diversity in representation. We argue that social media allow a more democratic view of the way cities are represented than other methodologies

APREGOAR: Development of a geospatial database applied to local news in Lisbon

Project Work presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceHá informações valiosas em formato de texto não estruturado sobre a localização, calendarização e a essências dos eventos disponíveis no conteúdo de notícias digitais. Vários trabalhos em curso já tentam extrair detalhes de eventos de fontes de notícias digitais, mas muitas vezes não com a nuance necssária para representar com precisão onde as coisas realmente acontecem. Alternativamente, os jornalistas poderiam associar manualmente atributos a eventos descritos nos seus artigos enquanto publicam, melhorando a exatidão e a confiança nestes atributos espaciais e temporais. Estes atributos poderiam então estar imediatamente disponíveis para avaliar a cobertura temática, temporal e espacial do conteúdo de uma agência, bem como melhorar a experiência do utilizador na exploração do conteúdo, fornecendo dimensões adicionais que podem ser filtradas. Embora a tecnologia de atribuição de dimensões geoespaciais e temporais para o emprego de aplicaçãoes voltadas para o consumidor não seja novidade, tem ainda de ser aplicada à escala das notícias. Além disso, a maioria dos sistemas existentes suporta apenas uma definição pontual da localização dos artigos, que pode não representar bem o(s) local(is) real(ais) dos eventos descritos. Este trabalho define uma aplicação web de código aberto e uma base de dados espacial subjacente que suporta i) a associação de múltiplos polígonos a representar o local onde cada evento ocorre, os prazos associados aos eventos, em linha com os atributos temáticos tradicionais associados aos artigos de notícias; ii) a contextualização de cada artigo através da adição de mapas de eventos em linha para esclarecer aos leitores onde os eventos do artigo ocorrem; e iii) a exploração dos corpora adicionados através de filtros temáticos, espaciais e temporais que exibem os resultados em mapas de cobertura interactivos e listas de artigos e eventos. O projeto foi aplicado na área da grande Lisboa de Portugal. Para além da funcionalidade acima referida, este projeto constroi gazetteers progressivos que podem ser reutilizados como associações de lugares, ou para uma meta-análise mais aprofundada do lugar, tal como é percebido coloquialmente. Demonstra a facilidade com que estas dimensões adicionais podem ser incorporadas com grade confiança na precisão da definição, geridas, e alavancadas para melhorar a gestão de conteúdo das agências noticiosas, a compreensão dos leitores, a exploração dos investigadores, ou extraídas para combinação com outros conjuntos dos dados para fornecer conhecimentos adicionais.There is valuable information in unstructured text format about the location, timing, and nature of events available in digital news content. Several ongoing efforts already attempt to extract event details from digital news sources, but often not with the nuance needed to accurately represent the where things actually happen. Alternatively, journalists could manually associate attributes to events described in their articles while publishing, improving accuracy and confidence in these spatial and temporal attributes. These attributes could then be immediately available for evaluating thematic, temporal, and spatial coverage of an agency’s content, as well as improve the user experience of content exploration by providing additional dimensions that can be filtered. Though the technology of assigning geospatial and temporal dimensions for the employ of consumer-facing applications is not novel, it has yet to be applied at scale to the news. Additionally, most existing systems only support a single point definition of article locations, which may not well represent the actual place(s) of events described within. This work defines an open source web application and underlying spatial database that supports i) the association of multiple polygons representing where each event occurs, time frames associated with the events, inline with the traditional thematic attributes associated with news articles; ii) the contextualization of each article via the addition of inline event maps to clarify to readers where the events of the article occur; and iii) the exploration of the added corpora via thematic, spatial, and temporal filters that display results in interactive coverage maps and lists of articles and events. The project was applied to the greater Lisbon area of Portugal. In addition to the above functionality, this project builds progressive gazetteers that can be reused as place associations, or for further meta analysis of place as it is colloquially understood. It demonstrates the ease of which these additional dimensions may be incorporated with a high confidence in definition accuracy, managed, and leveraged to improve news agency content management, reader understanding, researcher exploration, or extracted for combination with other datasets to provide additional insights

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

An Anthropology of Intellectual Exchange: Interactions, Transactions and Ethics in Asia and Beyond

Dialogues, encounters and interactions through which particular ways of knowing, understanding and thinking about the world are forged lie at the centre of anthropology. Such ‘intellectual exchange’ is also central to anthropologists’ own professional practice: from their interactions with research participants and modes of pedagogy to their engagements with each other and scholars from adjacent disciplines. This collection of essays explores how such processes might best be studied cross-culturally. Foregrounding the diverse interactions, ethical reasoning, and intellectual lives of people from across the continent of Asia, the volume develops an anthropology of intellectual exchange itself

Influence of geographic biases on geolocation prediction in Twitter

Geolocating Twitter users --- the task of identifying their home locations --- serves a wide range of community and business applications such as managing natural crises, journalism, and public health. While users can record their location on their profiles, more than 34% record fake or sarcastic locations. Twitter allows users to GPS locate their content, however, less than 1% of tweets are geotagged. Therefore, inferring user location has been an important field of investigation since 2010. This thesis investigates two of the most important factors which can affect the quality of inferring user location: (i) the influence of tweet-language; and (ii) the effectiveness of the evaluation process. Previous research observed that Twitter users writing in some languages appeared to be easier to locate than those writing in others. They speculated that the geographic coverage of a language (language bias) --- represented by the number of locations where the tweets of a specific language come from --- played an important role in determining location accuracy. So important was this role that accuracy might be largely predictable by considering language alone. In this thesis, I investigate the influence of language bias on the accuracy of geolocating Twitter users. The analysis, using a large corpus of tweets written in thirteen languages and a re-implemented state-of-the-art geolocation model back at the time, provides a new understanding of the reasons behind reported performance disparities between languages. The results show that data imbalance in the distribution of Twitter users over locations (population bias) has a greater impact on accuracy than language bias. A comparison between micro and macro averaging demonstrates that existing evaluation approaches are less appropriate than previously thought. The results suggest both averaging approaches should be used to effectively evaluate geolocation. Many approaches have been proposed for automatically geolocating users; at the same time, various evaluation metrics have been proposed to measure the effectiveness of these approaches, making it challenging to understand which of these metrics is the most suitable for this task. In this thesis, I provide a standardized evaluation framework for geolocation systems. The framework is employed to analyze fifteen Twitter user geolocation models and two baselines in a controlled experimental setting. The models are composed of the re-implemented model and a variation of it, two locally retrained open source models and the results of eleven models submitted to a shared task. Models are evaluated using ten metrics --- out of fourteen employed in previous research --- over four geographic granularities. Rank correlations and thorough statistical analysis are used to assess the effectiveness of these metrics. The results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from a geolocation system experiment, potentially leading experimenters to contradictory results about relative effectiveness. For general evaluations, a range of performance metrics should be reported, to ensure that a complete picture of system effectiveness is conveyed. Although a lot of complex geolocation algorithms have been applied in recent years, a majority class baseline is still competitive at coarse geographic granularity. A suite of statistical analysis tests is proposed, based on the employed metric, to ensure that the results are not coincidental

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field