© 2025 SURF
While there is extensive research on how Russian interference – in particular Russian disinformation operation – has played out in different European countries, indications of Russian interference directly targeting EU, its institutions or policies received little attention. This paper argues why there is good reason to assume that the EU, its institutions and its policies are an ideal a target for authoritarian regimes to exploit. It then explores in what ways, if any, Russian disinformation campaigning targeted EU institutions and their policies during the political and electoral campaigns leading up to the European Parliament (EP) elections of May 2019. In this context disinformation campaigning in terms of both network flows and contents (‘narratives’) have been examined, on the basis of a review of various reports identifying Russian interference and disinformation and of analyses of overall disinformation flows in Europe and the use of a database monitoring occurrences of disinformation.
MULTIFILE
Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
DOCUMENT
During an interview at Georgetown University’s School of Foreign Service one student questioned Prime Minister Rutte about an official apology for slavery. The Dutch Prime Minister assured that each island-nation to whom the Kingdom apologized “has full power to decide to leave the Kingdom. They are not colonized. They are independent.” Rutte described the current role of The Netherlands as that of a “gateway” to bring their products to Europe. The emphasis on trade relationship smacks of neo-colonial interests. Rutte’s portrayal of The Netherlands acting as the “in” to the European market for the former colonies is far from the recovery that one would expect for the descendants of the enslaved. In fact, the Slavery Past Dialogue made a number of recommendations to the Dutch Kingdom, including “active prevention of discrimination and institutional racism throughout society” and “the establishment of a Kingdom Fund […] for structural and sustainable financing of recovery measures.” The Dutch Prime Minister’s comments belie a singular focus on trade with the Caribbean nations rather than a holistic approach, looking at non-pecuniary interests involving the well-being of the descendants and the societies in which they live today. The “republicanization” serves as a backdrop to the years-long journey during which the Dutch government (and the Dutch crown) seemingly dragged their feet, refusing to issue a formal apology for the trade of Africans by the Dutch West Indies corporation. That much-solicited apology was finally issued in December 2022, despite warnings that any gesture that excluded reparations would not be favorably received by the Dutch Caribbean nations.
MULTIFILE
Deze rede gaat over het verrichte werk en de eerste vervolgplannen van het NHL Stenden Thorbecke Academie lectoraat ‘Bestuur in een Digitaliserende Samenleving’. Dit lectoraat werkt actief samen en vormt samen met het lectoraat ‘digitale weerbaarheid van mens en organisatie’ en de cross-over ‘digital citizenship’ de onderzoeksgroep Cybersafety.
DOCUMENT
![Bestuurlijk koorddansen in een digitaliserende samenleving [inaugurele rede]](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20250506135031692598.ijk20koorddansen20in20een20digitaliserende20samenleving-thumbnail-400x300.png)
The future of age-friendly cities and communities (AFCC) needs to adapt and be more agile to the changing needs of residents of all ages. The UN Decade of Healthy Ageing ‘the Decade' provides a unique opportunity to further strengthen age-friendly environments. The Decade brings together governments, civil society, international agencies, professionals, academics, the media and the private sector for 10 years of concerted action to improve the lives of older people, their families and the communities in which they live. This editorial serves as a thought piece and outlines recommendations for the imminent and future discourse surrounding digital transformation, digital skills/literacy and financial implications on societal citizens in the AFCC discourse. Action is needed now, and this can only be achieved by talking openly about the real issues and concerns affecting people in our communities and in the future.
MULTIFILE
From the traditional and pragmatic perspective on European cooperation shared by the Dutch political establishment, the French initiative for the Conference on the Future of Europe (CoFoE) was initially met with scepticism. Yet, during the experiment, the Dutch government and parliament translated their initial reluctance into assertive involvement. Rapporteurs from the bicameral parliament of the Netherlands became actively involved in CoFoE. They used it as an opportunity structure to pursue their political interests, which came down to watering down too-ambitious text proposals and stressing that the active participation of the citizens should be taken seriously. This chapter shows how both Houses used a wide range of parliamentary instruments – rapporteurs, delegations, plenary debates, committee hearings, questions, and a parliamentary citizens’ consultation – to debate, scrutinise and influence the CoFoE. Representatives and staff actively engaged in inter-parliamentary information exchange. In preparation for the plenaries, a sense of ‘esprit de corps’ developed between Dutch government representatives, members of parliament (MPs), Members of the European Parliament (MEPs), and supporting staff. This resulted in a remarkably coherent all-Dutch positioning up until the closure of the Conference and shared disappointment on the lack of a follow-up.
MULTIFILE
Social networks and news outlets use recommender systems to distribute information and suggest news to their users. These algorithms are an attractive solution to deal with the massive amount of content on the web [6]. However, some organisations prioritise retention and maximisation of the number of access, which can be incompatible with values like the diversity of content and transparency. In recent years critics have warned of the dangers of algorithmic curation. The term filter bubbles, coined by the internet activist Eli Pariser [1], describes the outcome of pre-selected personalisation, where users are trapped in a bubble of similar contents. Pariser warns that it is not the user but the algorithm that curates and selects interesting topics to watch or read. Still, there is disagreement about the consequences for individuals and society. Research on the existence of filter bubbles is inconclusive. Fletcher in [5], claims that the term filter bubbles is an oversimplification of a much more complex system involving cognitive processes and social and technological interactions. And most of the empirical studies indicate that algorithmic recommendations have not locked large segments of the audience into bubbles [3] [6]. We built an agent-based simulation tool to study the dynamic and complex interplay between individual choices and social and technological interaction. The model includes different recommendation algorithms and a range of cognitive filters that can simulate different social network dynamics. The cognitive filters are based on the triple-filter bubble model [2]. The tool can be used to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions on recommender systems can mitigate adverse side effects like the presence of filter bubbles. The resulting tool is an open-source interactive web interface, allowing the simulation with different parameters such as users' characteristics, social networks and recommender system settings (see Fig. 1). The ABM model, implemented in Python Mesa [4], allows users to visualise, compare and analyse the consequence of combining various factors. Experiment results are similar to the ones published in the Triple Filter Bubble paper [2]. The novelty is the option to use a real collaborative-filter recommendation system and a new metric to measure the distance between users' innate and final opinions. We observed that slight modifications in the recommendation system, exposing items within the boundaries of users' latitude of acceptance, could increase content diversity.References 1. Pariser, E.: The filter bubble: What the internet is hiding from you. Penguin, New York, NY (2011) 2. Geschke, D., Lorenz, J., Holtz, P.: The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology (2019), 58, 129–149 3. Möller, J., Trilling, D., Helberger, N. , and van Es, B.: Do Not Blame It on the Algorithm: An Empirical Assessment of Multiple Recommender Systems and Their Impact on Content Diversity. Information, Communication and Society 21, no. 7 (2018): 959–77 4. Mesa: Agent-based modeling in Python, https://mesa.readthedocs.io/. Last accessed 2 Sep 2022 5. Fletcher, R.: The truth behind filter bubbles: Bursting some myths. Digital News Report - Reuters Institute (2020). https://reutersinstitute.politics.ox.ac.uk/news/truth-behind-filter-bubblesbursting-some-myths. Last accessed 2 Sep 2022 6. Haim, M., Graefe, A, Brosius, H: Burst of the Filter Bubble?: Effects of Personalization on the Diversity of Google News. Digital Journalism 6, no. 3 (2018): 330–43.
MULTIFILE
