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Large technology companies hold unprecedented political, economic and social powers, given the ubiquity of recommendation systems in social networks, video and music streaming services, e-commerce sites and several other services. These systems prioritize metrics such as engagement and clicks, which end up inadvertently amplifying sensational and misleading content, compromising society's access to reliable sources, information and issues of public interest.


The main platforms today are based on algorithms that perform an editorial function. They are guided by criteria defined and programmed by humans in order to select the content exposed to users. Given the lack of transparency, the political and economic interests of companies are covered by the technical varnish of the algorithms they use. On the other hand, content producers constantly seek to adapt their production and distribution practices to the opaque rules of these systems, at the risk of having their reach and funding undermined by the platform if they do not adapt.

In addition, recommender systems are characterized by microsegmentation, using data produced by the users themselves as input for the accuracy and relevance of recommendations. Faced with the lack of transparency in methods used in the areas of data mining, big data and artificial intelligence, various studies have exposed incidences of worrying biases being reinforced by these algorithms. ​ Despite a friendly and conciliatory attitude, technology companies have been accused of making research and data auditability difficult, preventing a better understanding of the biases of recommendation systems. Academic literature has criticed the self-regulation and transparency initiatives of online platforms, pointing out the need for audits that could shed light on the internal workings of the algorithms used.


The internet has revolutionized the scale of production, access and sharing of cultural products such as music and videos. In this project we analyze the uses and social, cultural and economic impacts of online intermediaries that classify and recommend content based on data on user behavior. 

The impact of digital technologies on cultural consumption

Computational advertising, automation and inauthentic behavior on social media

The study of inauthentic interactions in social media, such as bots, cyborgs and trolls, is at the center of research on social manipulation strategies in digital networks. In this project, we investigate the practices and tools involved in the technological race in computational advertising campaigns.

Recommender systems and their impact on online information circulation

The recommendation systems of online platforms have a great influence on users' choices, indicating relevance, generating visibility and endorsing content. In this project, we study the editorial and advertising functions performed by these algorithms, investigating the criteria and the impact of the recommendation.

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