Upset by the absence of mainstream media coverage of Kenya’s post-election violence of 2008—in which almost a thousand people died in attacks on different ethnic communities—Ory Okolloh and three other tech-savvy activists called Erik Hersman, Juliana Rotich and David Kobia set up a platform, which they named ‘Ushahidi’ (‘testimony’ in Swahili), to visualise the dead and plot the violence reported via email and text message by surviving victims and relatives on a Google Maps chart (Keim 2012).
They decided not to build a tool from scratch, but to combine the capacities of mobile phones, databases, email systems and online cartography to develop the website quickly (Keim 2012; Adewumi 2008). In the next few months, others joined into establish a mechanism to verify eyewitness testimonies comparing them with media reports and sources from non-governmental organisations (NGOs), aid groups and governments, to avoid spreading false information (Smith 2008). Patrick Meier became Ushahidi’s Director of Crisis Mapping.1 By 2009, the Ushahidi platform had been used to monitor elections in Kenya, India and Mexico, and to track medical supply shortages in Malawi and Zambia (Bernholz et al. 2010). And in 2010, the Ushahidi Haiti map would revolutionise the traditional way in which emergencies, disasters and conflicts are tackled (ibid.), giving birth to digital humanitarianism.
Paraphrasing Ian C. Jarvie and Ulrich Beck (2015, 2017), these initial deployments of the Ushahidi platform illustrate people’s search for mapping systems that can help navigate a metamorphosing world. This book focuses precisely on novel social practices enabled by technology and data, which take a critical approach towards datafication and use it politically and proactively for meaning-making, coordination, participation and social change, for which Milan and I have coined ‘proactive data activism’ (2015, 125). Ushahidi’s crowdsourcing platform—which combines data and communication infrastructures and the geoweb, or geographic, geospatial and geotag overlay systems (Scharl and Tochtermann 2007)—is globally used to make sense of complex, ongoing crises and support the relief efforts. Some deployments of the Ushahidi platform are employed in this study as pivotal cases to illuminate how proactive data activism works in real life.
This study represents a contribution to our understanding of the interplay between data, technology and communicative practices on the one hand, and democratic participation on the other. It addresses the emergence of proactive data activism, which consists in ways of collaborating, organising and taking action via software and data, seeking to create unconventional narratives and solutions to social problems. By becoming involved in these activities, proactive data activists bypass deadlocks and top–down approaches to social challenges; they correct asymmetries and empower individuals and groups to communicate, collaborate and participate in decision-making processes.
In this chapter, I offer a conceptual toolkit to explore proactive data activism, which sits at the junction of other social uses of data (e.g. data journalism), other change-promoting activities (e.g. transnational activism) and other applications of the data infrastructure (e.g. the employment of the internet of things, IoT, in architecture). Thus, the approach of this study is multidisciplinary.
Here I look into big data in light of activism, and activism in light of big data, as well as the challenges and opportunities that the data infrastructure poses. Other notions are developed as the book progresses. These concepts derive from critical thought, international relations studies, journalism research, alternative and citizens’ media studies, critical cartography, and social movement and communication theory. Nevertheless, this is not a literature review; I draw from the empirical observations of 40 cases and dozens of interviews with practitioners, researchers, data activists and journalists, who spoke to me about their work and whose words I invoke whenever suitable.
Next, in this introduction a chart is offered to navigate the rest of the book, detailing the contents of the chapters and the analytical thread linking them, followed by a brief note on the methodology that sustains the analysis for this book.
Some words of caution are in order. What follows is neither an exhaustive account of existing data activist initiatives nor a normative classification of cases. I am biased by the languages I speak, the experience I have and the interests I pursue, so the voices included here come mostly from Europe, America and Africa to the detriment of other regions, and focus on issues such as human rights, humanitarianism, climate change and the environment. What I offer is an analysis of real-life cases and a taxonomy that can be applied to other situations not included in this study.
Finally, the do-goodism of the projects inspected here does not compel me to turn a blind eye to the contradictions that surround real-life cases. Lessons learnt are offered as a way of building a model for activism that can deal realistically with the challenges of the big data society. I am not enthralled by an individual tool or initiative either. The Ushahidi platform (experiencing a leadership crisis at the time of writing) is no longer the grassroots endeavour that it was when it started. Artefacts are not cure-alls without people, transparency, collaboration and engagement.
A Toolkit for Data Activism: Setting the Scene
Big Data in Light of Data Activism
Data activism is activism that utilises the data infrastructure as an enabling method. Definitions of big data are not unproblematic.
Big data can be facts, signals or symbols; that is, modifiable, distributed and interactive artefacts. Big data could be defined as a profusion of digital objects; user-generated online content; data resulting from datafication, or the ability to transform into data aspects of the world ‘that have never been quantified before’ (e.g. friendships in the form of likes); data generated by the IoT; signals captured and emitted by sensors, drones and portable devices; online traces left behind by web clickstreams and indexing processes; and metadata gained by the ‘snooping’ machinery, which are so large, can be processed with such velocity, are so varied, have so much potential when rendered useful and show so much accuracy and complexity that they can be considered big and, therefore, can only be managed using a new infrastructure (Kallinikos et al. 2013; Mayer-Schönberger and Cukier 2013, 5; International Telecommunication Union 2014). The data infrastructure is to be understood as a digital organisation that enables data sharing, management, storage, analysis and usage, which can include software and the platforms that allow the transfer and employment of data (Russom 2013, 4–20).
Big data analysis can produce new insights, new knowledge and new value (Lazer et al. 2009; Lohr 2012). The ability to generate and take in ever larger amounts of data has driven Joseph M. Hellerstein to speak of ‘the industrial revolution of data’ (2008).
The relevance of metadata became apparent in 2013, when Edward Snowden, a US computer analyst formerly at the Central Intelligence Agency (CIA), provided several media outlets with top-secret National Security Agency (NSA) documents, leading to exposés about massive surveillance of the phone and internet communications of ordinary people. According to Snowden’s revelations, the NSA requested metadata about millions of phone calls from Verizon, without informing its clients (Greenwald 2013). Thomas Poell, Helen Kennedy and José van Dijck label this practice dataveillance (2015). Despite being as ‘cooked’ as data (Boellstorff 2013), met...