Engineering the public: Big data, surveillance and computational politics

By Zeynep Tufekci. 2014

355 citations recorded by the source corpus.

Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns.

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

In Social Sciences & Humanities

Citations in the corpus

Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science (2017, background): “5,25,26 While lack of contextual understanding is often at the center of big data critiques, context is also a central concept for the work of human computer interaction, ubiquitous computing, and data science.”
Using a Personality-Profiling Algorithm to Investigate Political Microtargeting: Assessing the Persuasion Effects of Personality-Tailored Ads on Social Media (2020, positive): “Scholars point to the risk of voter manipulation (Zuiderveen Borgesius et al., 2018), following from an information asymmetry between the political actor and the citizen (Tufekci, 2014).”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, neutral): “…in the context of civil movements (Howard, et al., 2011; González-Bailón, et al., 2011; Tufekci & Wilson, 2012; González-Bailón, et al., 2013; Tufekci, 2014; Bastos, et al., 2014), political outreach (Bond, et al., 2012; Bakshy, et al., 2015), public health interventions (Centola, 2010;…”
Data Capitalism: Redefining the Logics of Surveillance and Privacy (2019, background): “…for society, which have taken shape in the forms of algorithmic discrimination, political influence hidden from public view, and expansion of state power into intimate domains of everyday life (Crawford & Schultz, 2014; Grimmelmann, 2009; Noble, 2016; Sifry, 2014; Tufekci, 2014; Zittrain, 2014).”
Algorithmic Accountability and Public Reason (2017, background): “Personalised platforms build detailed profiles of their user’s attributes and behaviour, which determine the content they view, the products they see and search results they receive (Tufekci, 2014; Sweeney, 2013).”
e-Tourism beyond COVID-19: a call for transformative research (2020, background): “Countless examples of such manipulative tools have been cited and discussed in recent critiques of those technologies that aim to persuade, surveille, and even “hijack” people’s mind (Harris 2016; Tufekci 2014).”
Dataset Featurization: Uncovering Natural Language Features through Unsupervised Data Reconstruction (2025, unknown): “The method could be applied to tasks such as de-anonymization (Narayanan and Shmatikov, 2008), amplification of existing biases (Barocas and Selbst, 2016), or enhancing the spread of misinformation (Tufekci, 2014).”