Social media dependency is a central mechanism through which digital vulnerability takes shape, making it critical to understand for research, design, and policy. This study distinguishes between functional dependency (needs-based reliance) and psychological dependency (compulsive engagement) and investigates how these dimensions intersect. We surveyed 873 adult users across Europe, measuring both dependency forms alongside demographics, well-being, motivations, platform choice, and exposure to manipulative design features. Latent profile analysis and multinomial logistic regression revealed five distinct dependency profiles: functional use, low-dependency pragmatic use, high-dependency social use, moderate-dependency hedonic use, and very high-dependency multi-motivated use. These findings show dependency is not uniform but layered and dynamic, shifting with users’ circumstances and socio-technical contexts. By situating dependency within both individual and design-related factors, the study advances theoretical debates on digital vulnerability and offers a profiles-based lens that helps inform the design of more autonomy-supportive social media platforms.

Content moderation is a vital condition that online platforms must facilitate, according to the law, to create suitable online environments for their users. By the law, we mean national or European laws that require the removal of content by online platforms, such as EU Regulation 2021/784, which addresses the dissemination of terrorist content online. Content moderation required by these national or European laws, summarised here as ‘the law’, is different from the moderation of pieces of content that is not directly required by law but instead is conducted voluntarily by the platforms. New regulatory requests create an additional layer of complexity of legal grounds for the moderation of content and are relevant to platforms’ daily decisions. The decisions made are either grounded in reasons stemming from different sources of law, such as international or national provisions, or can be based on contractual grounds, such as the platform's Terms of Service and Community Standards. However, how to empirically measure these essential aspects of content moderation remains unclear. Therefore, we ask the following research question: How do online platforms interpret the law when they moderate online content? To understand this complex interplay and empirically test the quality of a platform's content moderation claims, this article develops a methodology that facilitates empirical evidence of the individual decisions taken per piece of content while highlighting the subjective element of content classification by human moderators. We then apply this methodology to a single empirical case, an anonymous medium-sized German platform that provided us access to their content moderation decisions. With more knowledge of how platforms interpret the law, we can better understand the complex nature of content moderation, its regulation and compliance practices, and to what degree legal moderation might differ from moderation due to contractual reasons in dimensions such as the need for context, information, and time. Our results show considerable divergence between the platform's interpretation of the law and ours. We believe that a significant number of platform legal interpretations are incorrect due to divergent interpretations of the law and that platforms are removing legal content that they falsely believe to be illegal (‘overblocking’) while simultaneously not moderating illegal content (‘underblocking’). In conclusion, we provide recommendations for content moderation system design that takes (legal) human content moderation into account and creates new methodological ways to test its quality and effect on speech in online platforms.

The AI Act represents a significant legislative effort by the European Union to govern the use of AI systems according to different risk-related classes, linking varying degrees of compliance obligations to the system’s classification. However, it is often critiqued due to the lack of general public comprehension and effectiveness regarding the classification of AI systems to the corresponding risk classes. To mitigate those shortcomings, we propose a Decision-Tree-based framework aimed at increasing robustness, legal compliance and classification clarity with the Regulation. Quantitative evaluation shows that our framework is especially useful to individuals without a legal background, allowing them to improve considerably the accuracy and significantly reduce the time of case classification.

When the word 'ethical' becomes synonymous with specious, you know that something is amiss. With each data governance scandal, with each creation of a corporate 'ethics board', 'ethical standards' seemingly lose a few more feathers, to the point of generating instant suspicion when invoked in any official report. We argue that a key challenge in this regard is to more precisely define the ethics-regulation interface. In order to do this, we first provide an overview of recent endeavours to develop ethical frameworks around technology. We then look at a successful process of refinement of the ethics-regulation interface: the case of healthcare ethics in the UK. The third section develops an account of what a more robust ethics-regulation interface could look like, which would support a process of cross-fertilisation between the political, ethical and legal approaches. Finally, the fourth and last section critically examines a ‘live’ implementation of such ethics-regulation interface, as put forward in Quebec's ‘Bill 29′.

What does research look like in practice? Aside from popular assumptions of how researchers are lonely isolated individuals sitting disconnected from the rest of the world enmeshed in thought, a considerable part of research involves working with data. Whether this data is quantitative, qualitative, gathered through experiments or involves writing code, all of this data is not just magically ‘invented’ out of thin air, but instead develops in a process of interaction with both human beings and technical systems. However only a small fragment of this process is presented to outside reviewers, the outputs and the framing often specifically designed to make a specific point. How the author got there, and which assumptions were made on the way and how these assumptions developed over time is seldom included in the final write-up. The following article argues that rather than just providing output data to be considered in research – or providing explanations for technical outcomes as is frequently proposed in computer science, accountability can only be developed by better understanding the research process. In order to do this, we suggest a series of mechanisms that can be built into existing research practices to make them more intelligible to outside reviewers and scholars. These mechanisms are designed to develop the accountability principle of the GDPR and ensure more accountable scientific research. As the GDPR recitals also explicitly references scientific research, an accountability by design approach to technology research is grounded both in the articles and recitals of the GDPR. By documenting the key elements of a narrative research story which explains not just what you believe to have discovered but also how researchers got there, it may also be possible to create better accountability mechanisms.