Guest Blog: How do the increasing use of digital and AI-technology impact protests?

 By Majsa Storbeck, Gabriele Jacobs, Marc Schuilenburg, and Robin van den Akker

Storbeck, M., Jacobs, G., Schuilenburg, M., & van den Akker, R. (2025). Surveillance experiences of extinction rebellion activists and police: Unpacking the technologization of Dutch protest policing. Big Data & Society, 12(1). https://doi.org/10.1177/20539517241307892 (Original work published 2025)

Are we witnessing a historic surge in protests? According to the Carnegie’s Global Protest Tracker, 2024 saw more than 160 protests erupting across the globe, from France to Venezuela and Serbia to Indonesia. Over the last 15 years, the number of demonstrations around the world has more than tripled, scholars say. Due to strained police forces and limited funds, law enforcement agencies are looking for new ways to manage these protests effectively – ensuring demonstrators have a safe space to exercise their fundamental democratic right to protest. At the same time, they must account for public order demands by municipalities in public space.

The ‘right to protest’ is legally a combination of the right to free expression (article 10 ECHR) and the right to assembly and association (article 11 ECHR). It is a cornerstone of democratic societies, allowing individuals to express their views and hold those in power accountable. Municipalities and police have therefore the positive obligation to maximally facilitate protests, according to EU case-law (see for instance Navalny v. Russia from 2018). This obligation extends to forms of civil disobedience, which are increasingly becoming a popular method of demonstrating. While the right to protest is not “absolute” – meaning it can be restricted by muncipalities for a variety of reasons, like the prevention of disorder, national security, public health (think: COVID protests), or the protection of the rights and freedoms of others – these restrictions must not impair the essence of the right. 

Against this backdrop, protest scholars have documented changes in so-called "protest policing" over the last decades, with increasing incorporation of smart technologies to enhance efficiency and efficacy. However, there are both pros and cons to these technologies, human rights experts say. In certain situations, it may be appropriate for law enforcement to use digital and AI-technologies, such as when they assist with preparedness by analyzing de-identified logistical information (e.g., estimated attendance, crowd density, and protest routes). On the other hand, certain technologies can give rise to a higher degree of intrusiveness vis-a-vis the privacy of demonstrators – such as facial recognition and digital image recording. The latter category, in particular, has led to a growing body of research examining the link between technology use and “chilling effects” – the legal term for when individuals refrain from protesting due to fear of repercussions that may follow. 

In AI-Maps, we tried to deepen the understanding of especially AI's effects on protest policing, a relatively unexplored area, by observing Extinction Rebellion protests in The Hague and interviewing a diverse range of stakeholders, including activists, police officers, media representatives, NGOs, and the Ombudsman. Our key findings, now published, confirm the link between technology and the “chilling effect” on protests. However, we also found new patterns of this chilling effect, characterized by two distinct features: (1) they are more sociological in nature (rather than solely legal), and (2) they impact both activists and police. As such, in adapting to the increasing technological landscape both police and activists exhibited also ‘hyper-transparency’ (extreme openness) and ‘hyper-alertness’ (extreme caution). These other forms of chilling effects, which have not been discussed in the literature, have equally serious societal implications - similar to those arising from activists not exercising their right to protest. 

We discuss many of these social implications in our article, but we can mention a few here as initial examples. First and foremost, hyper-transparency and hyper-alertness are contrasting experiences—complete opposites—which impact dynamics at and beyond protests. These are tensions that movements must increasingly navigate in their culture—whether to balance/prioritize hyper-alertness or hyper-transparency—and to what extent this influences protest potency. The negative impact is not limited to activists, however: police officers increasingly find themselves viewed as representations of the government's failures or even “enemies” of demonstrators, instead of the traditional role of facilitators of protest rights and bridge-builders between activists and the government. When police start to wear balaclavas for privacy reasons (under hyper-alertness), for example, the perception of police as threatening for protestors can strengthen.

Our article provides concrete recommendations focused on de-escalating tensions and addressing deepening societal divisions—offering an alternative to the escalatory measures proposed by many MPs during the Dutch national debate on the right of protest on 22 January 2025. We principally argue that the (non-)use of AI requires increased and straightforward transparency. A suggestion would be to provide online information about the tools, their purpose, how they are used, and the legal basis for any technology-driven surveillance. This transparency is essential for dispelling fears of “unchecked” police discretion and reinforcing that policing is grounded in the rule of law and democratic oversight. We also believe technology legal frameworks should be developed collaboratively with policymakers, police, civil society, and privacy scholars: while the EU AI-Act provides some foundation, its ambiguities permit (too) much police discretion. The Dutch government's roundtable on protest rights (with XRNL, police and mayor present), was therefore a very promising example. Such democratic formats could be extended to reflections on technology experiences; as our research reveals, the perception of surveillance technology ripples through society, influencing public values far beyond protest rights alone.

Please reach out with thoughts, questions or comments you may have. 

We’d be delighted to hear what you think. 

Guest Blog: Synthetic ethnography: Field devices for the qualitative study of generative models

 By Gabriele de Seta, Matti Pohjonen, Aleksi Knuutila

de Seta, G., Pohjonen, M., & Knuutila, A. (2024). Synthetic ethnography: Field devices for the qualitative study of generative models. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241303126 (Original work published 2024)

The concept of synthetic ethnography emerged quite organically over time as we discussed our recent research projects and experimental approaches to ethnographic research. We are all social scientists coming from similar disciplinary backgrounds, and in the early 2020s we were all starting to do research about the social and cultural implications of machine learning, generative artificial intelligence, and synthetic media. We each use different combinations of qualitative and quantitative methods with experimental creative strategies, and we wanted to formalize a methodological approach to machine learning models, particularly the ones that power generative AI tools. The main argument of our article is that ethnographic methods can be not only useful to study the development, deployment and use of generative AI models, but that they can also actively experiment with these technologies, turning them into research tools. To support our argument, we combine two methodological traditions: digital methods, and experimental ethnography. From digital methods, we build upon Richard Rogers’ intuition that digital media can be repurposed into research tools to study their own functioning. From experimental ethnography, we draw on Tomás Sánchez Criado and Adolfo Estalella’s conceptualization of “field devices” – inventive social and material techniques used to anchor fieldwork. Synthetic ethnography combines these two approaches, arguing that qualitative analyses of generative AI models can be complemented with the repurposing of these research objects into experimental tools. After making a case for our methodological proposal, our article showcases three practical examples of synthetic ethnography in action, each based on one author’s research project. These include an ethnographic study of deepfakes revolving around first-hand experiences of synthetic media creation; an autoethnographic exploration of ethnic representation in text-to-image model training data; and the development of an interactive tool supporting ethnographic walks into the latent spaces of machine learning models.  

Call for Special Theme Proposals for Big Data & Society (Due March 31st, 2025)

 Call for Special Theme Proposals for Big Data & Society (Due March 31st, 2025)

The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for Special Themes to be published in 2026. BD&S is a peer-reviewed, interdisciplinary, scholarly journal that publishes interdisciplinary social science research about the emerging field of Big Data practices and how they are reconfiguring relations, expertise, methods, concepts and knowledge across academic, social, cultural, political, and economic realms. BD&S moves beyond usual notions of Big Data to engage with an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as extensiveness, granularity, automation, and complex analytics including data linking and mining. The journal attends to digital content generated through online and offline practices, including social media, search engines, Internet of Things devices, and digital infrastructures across closed and open networks, from commercial and government transactions to digital archives, open government and crowd-sourced data. Rather than settling on a definition of Big Data, the journal makes this an area of interdisciplinary inquiry and debate explored through multiple disciplines and themes.

Special Themes can consist of a combination of Original Research Articles (6 maximum, up to 10,000 words each), Commentaries (4 maximum, 3,000 words each) and one Editorial Introduction (3,000 words). All Special Theme content will have the Article Processing Charges waived. All submissions will go through the journal’s standard peer review process.

While open to submissions on any theme related to Big Data, we particularly welcome proposals on topics that the journal has not previously published extensively. You can find the full list of special themes published by BD&S at http://journals.sagepub.com/page/bds/collections/index

Format of Special Theme Proposals
Researchers interested in proposing a Special Theme should submit an outline with the following information.

• An overview of the proposed theme, including how it relates to existing research and the aims and scope of the journal, and the ways it seeks to expand critical scholarly research on Big Data.
• A list of titles, abstracts, authors, and brief biographies. For each, the type of submission (ORA, Commentary) should also be indicated. If the proposal is the result of a workshop or conference, that should also be indicated.
• Short bios of the Guest Editors, including affiliations and previous work in the field of Big Data studies. Links to homepages, Google Scholar profiles, or CVs are welcome, although we don’t require CV submissions.
• A proposed timing for submission to Manuscript Central. This should be in line with the timeline outlined below.

Information on the types of submissions published by the journal and other guidelines is available at https://journals.sagepub.com/author-instructions/BDS.

Timeline for Proposals

Submit proposals by March 31, 2025, via this online form: https://forms.gle/Fb3LDLP7To35j2gc9
(Note: You must have a Google account in order to access this form). Do not send proposals via email, as they will not be reviewed. The Editorial Team of BD&S will review proposals and make a decision by early May 2025. For selected proposals, manuscripts would be submitted to the journal (via Manuscript Central) by or before September 15, 2025.

For further information or to discuss potential themes, please contact Dr. Matthew Zook at zook@uky.edu.

Guest Blog: What holds the field of AI together?

by Glen Berman, Kate Williams, and Eliel Cohen

Berman, G., Williams, K., & Cohen, E. (2025). The benefits of being between (many) fields: Mapping the high-dimensional space of AI research. Big Data & Society, 12(1). https://doi.org/10.1177/20539517241306355

Artificial Intelligence is being mobilized across the university sector to coordinate a wide range of research programs and activities. AI is ‘in’, and nobody wants to be left out. But this begs the question: what is it about AI—as a discursive concept—that makes it so adaptable? What is it about the notion of AI that enables such a heterogenous array of actors—computer scientists, climate scientists, roboticists, ethicists, lawyers, artists, and more—to enroll it in their efforts to attract resources, coordinate research projects, and communicate research impacts? And, across this heterogenous network, what, if anything, lends the field of actors associated with AI coherence and stability?

In our paper, we begin to answer these questions. Through semi-structured interviews (n = 90) with academics affiliated with AI research networks in the UK, US, and Australia, we explore how notions of the ‘AI researcher’ and the ‘AI research field’ are constructed. Through our empirical account, we describe an AI research field that is uncertain and unstable. The field emerges at the intersection of multiple overlapping boundaries between disciplines, between academia, industry and government, between national and international hierarchies. And, the field is marked by commitments to highly applied, interdisciplinary and intersectoral research activities. Within this context, notions of AI are strategically mobilized by AI researchers and AI research networks to position themselves as intermediators at these intersections. In this light, we argue, that the definitional messiness associated with AI is a strategic commitment of the field—the fact that there are no shared or bounded definitions of what constitutes AI helps enable AI researchers to move between disciplines and sectors.

Yet, not anyone can claim to be an AI researcher. To be legitimized in a high dimensional field requires cultivation of an expertise that can be readily translated into the evaluation structures of many different fields. AI researchers’ commitment to applied research can be interpreted as one strategy for achieving this. Applied research can meet the demands of government funders and industry partners, can translate into news media reporting, and can be published in parallel academic disciplines. And, in the absence of formal qualifications or training programs, legitimization relies on affiliation with organizations or institutions whose value readily translates across fields. For individual researchers and for university administrations, then, establishing new, explicitly intersectoral and interdisciplinary AI-branded research networks is a self-reinforcing response to the high dimensionality of the AI research field.

Guest Blog: Exploring Digital Platforms in Agriculture: Oligopolistic Platformization and Its Impacts

Sauvagerd, M., Mayer, M., & Hartmann, M. (2024). Digital platforms in the agricultural sector: Dynamics of oligopolistic platformisation. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241306365

The agricultural sector is undergoing a profound digital transformation. Unlike other industries where new entrants disrupt existing markets, agriculture presents a unique phenomenon which we term “oligopolistic platformization.” Here, multinational upstream agribusinesses like John Deere and Bayer collaborate with Big Tech giants such as Microsoft, Amazon, and Google to integrate digital tools into farming while consolidating their market dominance.

Agribusinesses have developed sectoral platforms offering specialized solutions like real-time field monitoring, weather analytics, and precision farming. These platforms often create “digital twins” of farms, seamlessly integrating data sources such as spatial, climatic, and machine-generated inputs. Big Tech, in turn, provides critical infrastructure – AI, cloud computing, and data analytics – that powers the scalability and innovation of these platforms.

However, this transformation comes with complexities. At the heart of the shift lies datafication (turning farm activities into actionable data), selection (deciding what data is shared and with whom), and commodification (monetizing insights derived from this data). While these processes generate value, they also consolidate power among a few key players, potentially marginalizing smaller startups and reducing farmers’ autonomy.

Our visualization maps out the economic collaborations between Big Tech, agribusinesses, startups, and public institutions to reveal the industry’s reliance on a small number of tech giants for digital infrastructure, underscoring the growing consolidation in this space. Major agribusinesses like John Deere, Syngenta, and BASF have formed partnerships with these infrastructure providers, increasingly blurring boundaries between seeds, agrochemicals, biotechnology, and digital agriculture.*

As digital platforms become integral to agriculture, it is critical to address issues such as power asymmetries, opportunities for smaller players, and equitable data-sharing. Future research should examine the evolving dynamics of oligopolistic platformization under regulations like the EU Data Act, which mandates data access and interoperability. 

* While not fully comprehensive, the visualization represents an overall picture of the economic relationships within the industry.

Seeking Assistant Editors for Big Data & Society

 The journal Big Data & Society (https://journals.sagepub.com/home/bds) is calling for expressions of interest in serving as an Assistant Editor (AE) for a term of 1 to 2 years. The goal of the journal's AEs program is providing opportunities for early stage scholars (most often Ph.D. students) to network and gain experience in the peer-review journal publishing process. Assistant editors will have opportunities to discuss research topics with journal editors and co-editors, will be invited to review for the journal, contribute to promoting the journal (via Twitter) and to other projects as they arise. Unfortunately the AE position does not come with any monetary stipend.

To express interest in becoming an Assistant Editor, please fill out the for available at this Google Form by January 10, 2025. If you have questions you can contact the Editor-in-Chief of the journal, Dr. Matthew Zook (zook@uky.edu).

You can also find more information about the editorial team and latest papers at the Journal's blog https://bigdatasoc.blogspot.com/ and website https://journals.sagepub.com/articles/BDS'. 

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Big Data & Society (BD&S) is an Open Access peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing, and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business and governmental relations, expertise, methods, concepts and knowledge. 

Guest Blog: Navigating Crisis with Joint-Sensemaking: Insights from China's Health Code

by Yang Chen

Qu, J., Chen, L., Zou, H., Hui, H., Zheng, W., Luo, J.-D., Gong, Q., Zhang, Y., Wen, T., & Chen, Y. (2024). Joint-sensemaking, innovation, and communication management during crisis: Evidence from the DCT applications in China. Big Data & Society, 11(3). https://doi.org/10.1177/20539517241270714

 

In times of crisis, technology plays a key role in shaping collective responses. The COVID-19 pandemic highlighted the potential of digital contact tracing (DCT) applications, such as China's Health Code, to manage public health challenges. However, these technologies are not just technical solutions; they are sociotechnical phenomena emerging from complex interactions among technology, society, and governance.

 

The study "Joint-sensemaking, innovation, and communication management during crisis" explores how stakeholders in China navigated these complexities. It challenges the conventional view of innovation diffusion as linear, proposing instead a model of joint-sensemaking—a collaborative process of meaning-making among diverse actors.

 

A significant aspect of this research is the application of the structural hole theory, which examines how certain stakeholders, like official media, act as connectors or "structural hole spanners" within the communication network. These entities bridge different groups, facilitating information flow and influencing public sentiment more directly than traditional two-step flow models suggest. This highlights their critical role in shaping how innovations are perceived and adapted during crises.

 

The article provides empirical insights through the analysis of over 113,000 Weibo posts, revealing two pathways of sensemaking: the Patching and Add-in paths. These pathways illustrate how different interventions shape public sentiment and technology acceptance. By focusing on the dynamic interactions and the bridging roles played by key stakeholders, the study offers a nuanced understanding of innovation diffusion in crisis contexts.

 

We describe how these insights can inform strategies for policymakers and public health authorities. By leveraging the influence of structural hole spanners like official media, stakeholders can foster acceptance and trust, ensuring that DCT technologies are effectively integrated into society. This approach underscores the importance of continuous assessment and adaptation of communication strategies to meet the evolving needs of the public.

 

Overall, the study compels us to rethink crisis management as a transdisciplinary endeavor, involving ongoing dialogue and collaboration across different sectors and perspectives. It shifts the focus from problem-solving to problem-opening, encouraging a more comprehensive and inclusive approach to managing innovations during crises.