Dr Cristina Alaimo

Lecturer in Digital Economy



Cristina Alaimo is a Lecturer (Assistant Professor) in Digital Economy at Surrey Business School, University of Surrey. She holds a Ph.D. in Information Systems from the Department of Management, London School of Economics and Political Science. Cristina is interested in investigating the dynamics and effects of social computing -the growing importance data on users assume in the digital economy. More specifically, her work examines mechanisms and consequences of data production and sharing within and across platforms and platform ecosystems. Recent publications include: “Encoding the Everyday: The Infrastructural Apparatus of Social Data” (2016, with Jannis Kallinikos, in: Big data is not a monolith: policies, practices, and problems. MIT Press) and “Computing the Everyday: Social Media as Data Platforms” (2017, with Jannis Kallinikos in The Information Society, 33/4).

My publications


Alaimo C, Kallinikos J (2017) Computing the everyday: Social media as data platforms, The Information Society 33 (4) pp. 175-191 Taylor & Francis
We conceive social media platforms as sociotechnical entities that variously shape user platform involvement and participation. Such shaping develops along three fundamental data operations that we subsume under the terms of encoding, aggregation, and computation. Encoding entails the engineering of user platform participation along narrow and standardized activity types (e.g., tagging, liking, sharing, following). This heavily scripted platform participation serves as the basis for the procurement of discrete and calculable data tokens that are possible to aggregate and, subsequently, compute in a variety of ways. We expose these operations by investigating a social media platform for shopping. We contribute to the current debate on social media and digital platforms by describing social media as posttransactional spaces that are predominantly concerned with charting and profiling the online predispositions, habits, and opinions of their user base. Such an orientation sets social media platforms apart from other forms of mediating online interaction. In social media, we claim, platform participation is driven toward an endless online conversation that delivers the data footprint through which a computed sociality is made the source of value creation and monetization.
Alaimo C, Kallinikos J (2016) Encoding the everyday: The infrastructural apparatus of social data, In: Sugimoto C, Ekbia H, Mattiolo M (eds.), Big Data is not a Monolith: Policies, Practices, and Problems pp. 77-90 MIT Press
Social data is the computable data footprint of user participation in social media. It is produced through the far-reaching standardization of social interaction that allows users to perform daily and en masse hyper-stylized activities such as ?tagging?, ?following? or ?liking?. The data thus produced are piled up and processed. The outputs of these operations (e.g. similarity, popularity or trending scores) are carried back to users in the form of personalized suggestions, embedded into platform functioning and shared with third parties through APIs and other boundary spanning technologies, reinforcing the process through which social interaction is made the engine of social data production. These conditions set social data apart from data generated through automated technologies of data tracking and recording, and outline the distinct nature of the contribution it makes to the developments associated with big data.
Alaimo Cristina, Kallinikos Jannis (2019) Recommender Systems, In: Beyes T., Holt R., Pias C. (eds.), The Oxford Handbook of Media, Technology and Organization Studies Oxford University Press (OUP)
Personalization increasingly mediates the experience of users on the Web. Online platforms and organizations use personalization services to retain users, achieve longer user or customer engagement and, ultimately, higher profits. Cast in this light, personalization is a ubiquitous modality by means of which organizations seek to structure interaction with their users. Amazon, for instance, mediates the buying experience of its customers through computational systems that advance recommendations concerning relevant products to buy upon nearly every transaction. Similarly, Spotify uses the listening habits of its users to recommend tunes which they may find relevant to listen. In a rather different context, Facebook modulates its news feed to the interests of individual users by mapping each user?s ongoing interaction with his/her network of other users, and Google famously personalizes its search engine results, gathering, in turn, relevant information on the search habits of users.
Alaimo Cristina, Kallinikos Jannis (2018) Objects, Metrics and Practices: An Inquiry into the Programmatic Advertising Ecosystem, In: Schultze U, Aanestad M, Mähring M, Østerlund C, Riemer K (eds.), Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performativity of Technology. IS&O 2018. IFIP Advances in Information and Communication Technology 543 (543) pp. 110-123 Springer
Programmatic advertising is a large scale, real-time bidding process, whereby ads are automatically assigned to available spaces across types of media and geographic regions upon an individual user?s browser request. The large-scale automation of programmatic advertising requires the establishment of standards and the development of technologies to govern the behavior of market participants (sellers, buyers, intermediaries). We present evidence on the rules of programmatic exchange and on the role played by a specific class of digital objects. We focus in particular on the metrics to which these objects are linked and how they define what is exchanged and the parameters of these exchanges. We furthermore demonstrate that the metrics and the technological complexes associated with them are constituted by the institutional field of digital advertising and its complex technological infrastructure. Rather than being simply means to monitor a pre-existing reality ?out there? (such as user or audience behavior) these metrics and techniques bring forward their own reality and heavily impact upon and shape the objects and processes of digital advertising.
Alaimo Cristina, Kallinikos Jannis (2019) Social Media and the Infrastructuring of Sociality, In: Lounsbury Michael (eds.), Thinking Infrastructures 62 pp. 289-306 Emerald
Social media stage online patterns of social interaction that differ remarkably
from ordinary forms of acting, talking and relating. To unravel these differences,
we review the literature on micro-sociology and social psychology and derive a
shorthand version of socially-embedded forms of interaction. We use that version
as a yardstick for reconstructing and assessing the patterns of sociality social
media promote. Our analysis shows that social media platforms stage highly
stylized forms of social interaction such as liking, following, tagging, etc. that
essentially serve the purpose of generating a calculable and machine-readable
data footprint out of user platform participation. This online stylization of social
interaction and the data it procures are, however, only the first steps of what we
call the infrastructuring of social media. Social media use the data footprint
that results from the stylization of social interaction to derive larger (and
commercially relevant) social entities such as audiences, networks and groups
that are constantly fed back to individuals and groups of users as personalized
recommendations of one form or another. Social media infrastructure sociality
as they provide the backstage operations and technological facilities out of
which new habits and modes of social relatedness emerge and diffuse across the
social fabric.