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Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media

In recent years, much has been written on 'big data' in both the popular and academic press. After the hubristic declaration of the 'end of theory' more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that 'big data' emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist 'accumulation by dispossession' that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of 'big data' within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualization, situating big data as a form of capitalist ...

In recent years, much has been written on 'big data' in both the popular and academic press. After the hubristic declaration of the 'end of theory' more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that 'big data' emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist 'accumulation by dispossession' that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of 'big data' within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualization, situating big data as a form of capitalist expropriation and dispossession stresses the urgent need for critical, theoretical understandings of data and society.

The Approved Researcher scheme is used by the United Kingdom Office for National Statistics to grant access to microdata that cannot be published openly. Following on from reviews of this scheme and of data that fall within its remit, there have been changes to the mechanisms by which the UK Data Service provides access to these data sources. These changes relate to the process of gaining permission to access data, and to a statistical disclosure review of the licences under which sensitive variables are held. Using these reviews as exemplars, this presentation will discuss how the impact of the changes affects the operation of the UK Data Service (in acquisition, licensing, ingest, access, and support) and how the user experience is altered in parallel. This exercise demonstrates the value of working closely with data depositors at all stages of the data lifecycle to strike a balance between preserving data security and ensuring that sensitive information can be shared safely and practically for legitimate research needs. As legislation and attitudes evolve to encompass new forms of data, there will be a continuing need for data producers and data services to provide dynamic responses to new developments.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Leveraging LiDAR and Street View data for road feature detection with OSNI The Ordnance Survey of Northern Ireland (OSNI) mission is to provide high quality geospatial data. Historically this has been for 2D mapping, but modern survey techniques and increasing user requirements have shifted focus toward 3D data. Since 2019, OSNI has operated a vehicle mounted Mobile Mapping System (Leica Pegasus:Two Ultimate Mobile Mapping System) across Northern Ireland capturing 3D Point Cloud data and spherical street view imagery. The range of potential applications is significant, including urban planning, asset identification and management, automating identification of road sign changes for navigation and transport network datasets, identifying feature locations such as scenic views, drainage, potholes and road surface quality, street furniture maintenance, 5G network planning and managing autonomous vehicles. While availability and accessibility of this kind of raw data is improving, there are significant technical challenges in deriving insights from the richness of this dataset. To address these challenges this project seeks to explore the potential of OSNI's highly detailed Light Detection and Ranging (LiDAR) and imagery data via Machine Learning (ML) and data science methods, with a focus on developing pipelines to visualise, classify and identify road features like drainage which could potentially help various government authorities better monitor road infrastructure. There are many other potential applications for the sort of data OSNI collects, and we hope some of the pipelines and visualisations explored below can aid broader applicability. Below are the results from each of the streams of work conducted.

PurposeThe purpose of this study is to address the generalised lack of guidance on ethical treatment of corporate (e.g. non-research) data in higher education institutions, by focusing on the case of the University of Queensland (Brisbane, Australia). No actionable framework is currently available in the country to govern the ethical usage of corporate data. As such, this research takes a stakeholder-centred approach to data ethics; the lived experience of the stakeholders involved coupled with a theory-based ethical framework allowed the authors build to build a framework to guide ethical data practice.Design/methodology/approachAdopting a revised canonical action research approach focused on intervention on the context, the authors conducted a review of the literature on ethical usage of data in higher education institutions; administered one survey to university students (n = 168); and facilitated three workshops with professional staff (two) and students (one).FindingsCollected data highlighted how, among other themes, the role and ethical importance of transparency was the dominant claim among all stakeholder groups. Findings helped the authors develop an Enhanced Enterprise Data Ethics Framework (EEDEF) emphasising transparency and stakeholder-centricity.Practical implicationsLegislation is the driver to regulate the use of corporate data in higher education; however, this can be problematic because legislation is retrospective, lacks normativity and offers scarce directions for cases that do not exactly follow within the legislative mandate. In light of these regulatory limitations, the authors' EEDEF offers operators guidance on how to ethically manage corporate data in the higher education environment.Originality/valueThis study fills gaps in praxis and theory; that is the lack of literature and guiding ethical frameworks to inform data practice in higher education. This research fosters a more ethical data management by virtue of genuine and authentic engagement with stakeholders and emphasises the importance of strategic decision-making and maturity of data culture in the higher education sector.