This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms’ ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users’ expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users’ agency. The fourth section introduces the concept of “algorithmic sovereignty.” Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable “algorithmic sovereignty.”

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithms usually lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements and an analytical model to capture the visibility of publishers over a News Feed. First, measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility bias incurred by the users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.

Algorithmic personalization is difficult to approach because it entails studying many different user experiences, with a lot of variables outside of our control. Two common biases are frequent in experiments: relying on corporate service API and using synthetic profiles with small regards of regional and individualized profiling and personalization. In this work, we present the result of the first crowdsourced data collections of YouTube's recommended videos via YouTube Tracking Exposed (YTTREX). Our tool collects evidence of algorithmic personalization via an HTML parser, anonymizing the users. In our experiment we used a BBC video about COVID-19, taking into account 5 regional BBC channels in 5 different languages and we saved the recommended videos that were shown during each session. Each user watched the first five second of the videos, while the extension captured the recommended videos. We took into account the top-20 recommended videos for each completed session, looking for evidence of algorithmic personalization. Our results showed that the vast majority of videos were recommended only once in our experiment. Moreover, we collected evidence that there is a significant difference between the videos we could retrieve using the official API and what we collected with our extension. These findings show that filter bubbles exist and that they need to be investigated with a crowdsourced approach.