Computational observation: Challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in

Abstract

A lot of modern media use is guided by algorithmic curation, a phenomenon that is in desperate need of empirical observation, but for which adequate methodological tools are largely missing. To fill this gap, computational observation offers a novel approach—the unobtrusive and automated collection of information encountered within algorithmically curated media environments by means of a browser plug-in. In contrast to prior methodological approaches, browser plug-ins allow for reliable capture and repetitive analysis of both content and context at the point of the actual user encounter. After discussing the technological, ethical, and practical considerations relevant to this automated solution, we present our open-source browser plug-in as an element in an adequate multi-method design, along with potential links to panel surveys and content analysis. Ultimately, we present a proof-of-concept study in the realm of news exposure on Facebook; we successfully deployed the plug-in to Chrome and Firefox, and we combined the computational observation with a two-wave panel survey. Although this study suffered from severe recruitment difficulties, the results indicate that the methodological setup is reliable and ready to implement for data collection within a variety of studies on media use and media effects.

To track or not to track: examining perceptions of online tracking for information behavior research

“…4. In fact, the majority of existing tracking tools are designed by research groups or individual researchers (Menchen-Trevino and Karr, 2012;Bod o et al, 2017;Haim and Nienierza, 2019;Adam et al, 2019;Van Damme et al, 2020;Krieter, 2019). While there are some research groups (Festic et al, 2021;Stier et al, 2020b) that rely on third-party software used by external market companies to collect data, there is a strong leaning toward self-designed tracking solutions within academic tracking research.…”

“…4. In fact, the majority of existing tracking tools are designed by research groups or individual researchers (Menchen-Trevino and Karr, 2012; Bodó et al ., 2017; Haim and Nienierza, 2019; Adam et al ., 2019; Van Damme et al. , 2020; Krieter, 2019).…”

Online News User Journeys: The Role of Social Media, News Websites, and Topics

“…In this way, context features relate to the conditions in which the tweet is posted, such as time and day of publication, location of the author, or geographical focus of the tweet. In line with recent work by Haim and Nienierza (2019), we address context as the way in which users encounter news. More specifically, we consider the type of website in which the news was encountered (e.g.…”

“…However, these approaches do not always provide insights into the actual individually encountered information and the access to information via different media (for an overview see e.g. Haim and Nienierza 2019).…”

The practical and ethical challenges in acquiring and sharing digital trace data: Negotiating public-private partnerships

“… 12. Another option that Halavais (2019) mentions is to recruit participants and ask them to install browser plugins (or other pieces of software) that collect some of their digital traces and make them available to the researchers (for an example, see Haim and Nienierza, 2019). …”