Zynga’s FarmVille, social games, and the ethics of big data mining

The increasing necessity of engaging in social interaction through online commercial providers such as Facebook, alongside the ability of providers to extract, aggregate, analyse, and commercialise the data and metadata such activities produce, have attracted considerable attention amongst the media and academic commentators alike. While much of the attention has been focused on the data mining of social networking services such as Facebook, it is equally important to recognise the widespread adoption of large-scale data mining practices in a number of realms, including social games such as the well-known FarmVille and its sequels, created by Zynga. The implicit contract that the public who use these services necessarily engage in requires them to trade information about their friends, their likes, their desires, and their consumption habits in return for their participation in the service. This paper will critically explore the realm of social games utilising Zynga as a central example, with a view to examine the practices, politics, and ethics of data mining and the inherent social media contradiction. In determining whether this contradiction is accidental or purposeful, this paper will ask, in effect, whether Zynga and other big data miners behind social games are entrepreneurial heroes, more sinister FarmVillains, or whether it is possible at all to draw a line between the two? In doing so, Zynga’s data mining approach and philosophy provide an important indicator about the broader integration of data analytics into a range of everyday activities

Photo editing: Enhancing social media images to reflect appearance ideals

Many of the images used in traditional forms of mass media have been modified to portray unrealistic and idealised beauty characteristics. Further to this, members of the general public have now begun to digitally enhance their own pictures for social media posts, in order to fulfil these often unattainable standards. Ella Guest explores the impact exposure to idealised images of peers may have on health and wellbein

Reflections on deploying distributed consultation technologies with community organisations

In recent years there has been an increased focus upon developing platforms for community decision-making, and an awareness of the importance of handing over civic platforms to community organisations to oversee the process of decision-making at a local level. In this paper, we detail fieldwork from working with two community organisations who used our distributed situated devices as part of consultation processes. We focus on some of the mundane and often-untold aspects of this type of work: how questions for consultations were formed, how locations for devices were determined, and the ways in which the data collected fed into decision-making processes. We highlight a number of challenges for HCI and civic technology research going forward, related to the role of the researcher, the messiness of decision making in communities, and the ability of community organisations to influence how citizens participate in democratic processes

Catching the flu: Syndromic surveillance, algorithmic governmentality and global health security

How do algorithms shape the imaginary and practice of security? Does their proliferation point to a shift in the political rationality of security? If so, what is the nature and extent of that shift? This article explores these questions in relation to global health security. Prompted by an epidemic of new infectious disease outbreaks – from HIV, SARS and pandemic flu, through to MERS and Ebola – many governments are making health security an integral part of their national security strategies. Algorithms are central to these developments because they underpin a number of nextgeneration syndromic surveillance systems now routinely used by governments and international organizations to rapidly detect new outbreaks globally. This article traces the origins, design and evolution of three such internet-based surveillance systems: 1) the Program for Monitoring Emerging Diseases, 2) the Global Public Health Intelligence Network, and 3) HealthMap. The article shows how the successive introduction of those three syndromic surveillance systems has propelled algorithmic technologies into the heart of global outbreak detection. This growing recourse to algorithms for the purposes of strengthening global health security, the article argues, signals a significant shift in the underlying problem, nature, and role of knowledge in contemporary security practices

Small Big Data: Using multiple data-sets to explore unfolding social and economic change

Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated (and to which additional data can be linked) and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy change at the national level alters the attitudes and experiences of citizens. Importantly, they are also ‘small’ in the sense that the task of rendering the data usable, linking it and decoding it, required both manual processing and tacit knowledge of the context of the data and intentions of its creators

Data, ideology, and the developing critical program of social informatics

The rapidly shifting ideological terrain of computing has a profound impact on Social Informatics's critical and empirical analysis of computerization movements. As these movements incorporate many of the past critiques concerning social fit and situational context leveled against them by Social Informatics research, more subtle and more deeply ingrained modes of ideological practice have risen to support movements of computerization. Among these, the current emphasis on the promises of data and data analytics presents the most obvious ideological challenge. In order to reorient Social Informatics in relation to these new ideological challenges, Louis Althusser's theory of ideology is discussed, with its implications for Social Informatics considered. Among these implications, a changed relationship between Social Informatics's critical stance and its reliance on empirical methods is advanced. Addressed at a fundamental level, the practice of Social Informatics comes to be reoriented in a more distinctly reflective and ethical direction

Forecasting in the light of Big Data

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann

Are the dead taking over Facebook? A Big Data approach to the future of death online

We project the future accumulation of profiles belonging to deceased Facebook users. Our analysis suggests that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, however, this number will exceed 4.9 billion. In both cases, a majority of the profiles will belong to non-Western users. In discussing our findings, we draw on the emerging scholarship on digital preservation and stress the challenges arising from curating the profiles of the deceased. We argue that an exclusively commercial approach to data preservation poses important ethical and political risks that demand urgent consideration. We call for a scalable, sustainable, and dignified curation model that incorporates the interests of multiple stakeholders

Problem-solving for problem-solving: Data analytics to identify families for service intervention

The article draws on Bacchi’s ideas about problematisation (2020) and links to technological solutionism as governing logics of our age, to explore the double-faceted problem-solving logic operating in the UK family policy and early intervention field. Families with certain characteristics are identified as problematic, and local authorities are tasked with intervening to fix that social problem. Local authorities thus need to identify these families for problem-solving intervention, and data analytics companies will solve that problem for them. In the article, we identify discourses of transmitted deprivation and anti-social behaviour in families and the accompanying costly public sector burden as characteristics that produce families as social problems, and discursive themes around delivering powerful knowledge, timeliness and economic efficiently in data analytic companies’ problem solving claims for their data linkage and predictive analytics systems. These discursive rationales undergird the double-faceted problem-solving for problem-solving logic that directs attention away from complex structural causes

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges