Abstract
The European Union (EU) Digital Services Act (DSA) has introduced a novel set of rules for online platforms and search engines, with significant implications for the Recommender Systems community. Through its data access mechanisms, the DSA invites researchers to request both publicly available and private data from Very Large Online Platforms (VLOPs) and Very Large Search Engines (VLOSEs) - those with more than 45 million active recipients in the EU - to investigate systemic risks associated with the dissemination of illegal content, risks to the exercise of fundamental rights, and negative effects on electoral processes, public health, and gender-based violence. This tutorial is aimed at researchers who are interested in submitting such data access requests and will provide them with the knowledge to do so by introducing the relevant definitions and provisions of the DSA, and addressing the most important procedural steps to obtain data access and will provide attendees with a comprehensive understanding of the DSA's data access implications for RecSys research. The tutorial targets researchers, practitioners, and students in understanding current developments in online platform regulation in Europe and their impact on RecSys research.

Abstract
The Digital Services Act (DSA) establishes a regulatory framework for online platforms and search engines in the European Union, focusing on mitigating systemic risks such as illegal content dissemination, fundamental rights violations, and impacts on electoral processes, public health, and gender-based violence. Very Large Online Platforms (VLOPs) and Very Large Search Engines (VLOSEs), defined as those with over 45 million active recipients, must provide data access for research to enable investigations into these risks and the development of solutions. This tutorial is tailored for the UMAP community, addressing the implications of the DSA for user modelling research. It will cover the DSA's key provisions and definitions, outline the procedural steps for accessing VLOP and VLOSE data, and discuss the technical aspects of data access requests. Participants will also explore the challenges and opportunities involved in working with this data. By the end of the tutorial, attendees will have a thorough understanding of the DSA's data access provisions, the technical and procedural requirements for accessing VLOP and VLOSE data, and the regulation's implications for user modelling research. They will be equipped to navigate the complexities of the DSA and contribute to the development of responsible and transparent online platforms.Further information and resources about the tutorial are available on the website: https://erasmopurif.com/tutorial-dsa-umap25/.

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Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission, the fifth requirement (“Diversity, non-discrimination and fairness”) declares: “In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle. [...] This requirement is closely linked with the principle of fairness”. In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature.

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This paper analyses the causal relationship between external events and sports content TV audiences. To accomplish this, we explored external data related to sports TV audience behaviour within a specific time frame and applied a Granger causality analysis to evaluate the effect of external events on both TV clients' volume and viewing times. Compared to regression studies, Granger causality analysis is essential in this research as it provides a more comprehensive and accurate understanding of the causal relationship between external events and sports TV viewership. The study results demonstrate a significant impact of external events on the TV clients' volume and viewing times. External events such as the type of tournament, match popularity, interest and home team effect proved to be the most informative about the audiences. The findings of this study can assist TV distributors in making informed decisions about promoting sports broadcasts.