Hereby the slides that we presented at the 2022 Surveillance & Society Rotterdam Conference. Each set of slides is accompanied by the related abstract.
Although surveillance capitalism - as intended by Shoshana Zuboff - is an emerging topic, it already attracted the attention of many scholars from different fields within social sciences. Therefore, in this contribution we propose a systematic literature review of the topic of surveillance capitalism. Specifically, we developed a systematic literature review on a pool of 161 academic articles automatically extracted (through a Python script) from ad hoc scientific sources (e.g., Scopus), which we processed with computational techniques of text analysis (e.g., co-word analysis, topic modelling, TF-IDF). Also, a close reading of a sample of 30 articles was conducted. Results show that the topic of surveillance capitalism is composed by six main sub-topics: marketing & social control, big data & datafication, platforms & platformization, data privacy & protection, culture of surveillance, AI. We argue that all these key sub-topics need to be addressed attentively (or at least taken into consideration) when dealing with academic research and/or writing on surveillance capitalism, also paying attention on how each dimension inform and co-construct each other.
Although surveillance capitalism is already well-established in advanced economies, we can argue that the current Covid-19 emergence has probably accelerate the diffusion of surveillance capitalism logics and infrastructures (e.g., platformization of higher education). Despite the pervasiveness and currency of this phenomenon, we still know very little about how the general public perceives and frames it. In particular, there is a shortage of empirical research on citizens’ opinions towards surveillance capitalism as well as their level of awareness about the processes of data exploitation and value extraction carried out by corporate platforms on the very data users produce through their everyday digital practices. To address this research gap, we developed an exploration (based on digital methods) on dataset of 302k Italian tweets (collected by following ad hoc keywords, such as ‘surveillance + Facebook’, ‘surveillance + iPhone’, etc). We analyzed this dataset combining computational and qualitative techniques – network analysis, topic modelling, ethnographic content analysis. Our preliminary results show that, on a general level, Twitter users seem unable to distinguish between processes of surveillance upon citizens and consumers (which they consider basically the same thing). Anyhow, on a micro level, specific communities of users tend to develop different narratives on surveillance capitalism, imagining different ‘models’ of it (such as, dystopian surveillance, benevolent surveillance, conspiracy surveillance, entertainment surveillance).
This research proposes a reflection on the implications of dataveillance (based on algorithms) and practices of countersurveillance in the healthcare field. Countersurveillance is the practice of making surveillance activities of institutions difficult or implementing technologies to evade surveillance altogether. Countersurveillance achieves its goal by subverting various components of the surveillance process and it has many applications. It can be used to protect privacy, civil liberties, and against abuses of surveillance. Additionally, it may be employed to push surveillance systems beyond their breaking point and in doing so it identifies potential vulnerabilities and points of error. Many countersurveillance techniques use human methods rather than electronic; these activities might include ‘evasion’ (e.g., avoiding risky locations, being discreet or using code words), ‘being situation-aware’; ‘hiding in secure locations’; and ‘concealing one’s identity’. Through this proposal we want to explore resistance practices and imaginaries applied to algorithmic surveillance in the health domain. Specifically, we explore the debate on the Immuni App on Reddit.