Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.

Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT systems, but also for a future where machines can model and mine events for themselves.

Event tracking literature based on Twitter does not have a state-of-the-art. What it does have is a plethora of manual evaluation methodologies and inventive automatic alternatives: incomparable and irreproducible studies incongruous with the idea of a state-of-the-art. Many researchers blame Twitter's data sharing policy for the lack of common datasets and a universal ground truth–for the lack of reproducibility–but many other issues stem from the conscious decisions of those same researchers. In this paper, we present the most comprehensive review yet on event tracking literature's evaluations on Twitter. We explore the challenges of manual experiments, the insufficiencies of automatic analyses and the misguided notions on reproducibility. Crucially, we discredit the widely-held belief that reusing tweet datasets could induce reproducibility. We reveal how tweet datasets self-sanitize over time; how spam and noise become unavailable at much higher rates than legitimate content, rendering downloaded datasets incomparable with the original. Nevertheless, we argue that Twitter's policy can be a hindrance without being an insurmountable barrier, and propose how the research community can make its evaluations more reproducible. A state-of-the-art remains attainable for event tracking research.