Primary obligations include obtaining explicit consent from the data subject for the processing of personal data and providing full transparency with respect to processing and sharing. With the coming into effect of the GDPR in May 2018, several tools [11, 16, 19] have recently been developed that can be used to assist companies to assess the compliance of their systems and processes with respect to obligations set forth in the GDPR. However, such tools are targeted at self assessment (i.e. companies complete standard questionnaires in the form of a privacy impact assessment) and cannot be used to automatically check compliance with usage constraints. Such, automated transparency and compliance mechanisms would require not only machine-readable representations of the users consent, but also machine-readable representations of data processing and sharing. SPECIAL 1 is an EU H2020 research and innovation action, which addresses these challenges by demonstrating how Semantic Web technologies can be used for both consent and personal data processing representation and compliance checking. In particular we devise a suite of ontologies and vocabularies that can be used to: (i) model data usage policies, conforming the SPECIAL's Usage Policy Language, (ii) represent data processing and sharing events in a semantic log. Both of which have been developed in close collaboration with legal experts, thus ensuring that our automated compliance checking is tightly coupled with the legal assessment process.1 https://www.specialprivacy.eu/ 1 arXiv:2001.09461v1

Social media systems such as weblogs, photo-and link-sharing sites, wikis, and online forums are currently thought to produce up to one third of new web content. One thing that sets these "web 2.0" sites apart from traditional web pages and resources is that they are intertwined with other forms of networked data. Their standard hyperlinks are enriched by social networks, comments, trackbacks, advertisements, tags, RDF data, and metadata. We describe recent work on building systems that use models of the blogosphere to recognize spam blogs, find opinions on topics, identify communities of interest, derive trust relationships, and detect influential bloggers. Their reach and impact is significant, with tens of millions of people providing content on a regular basis around the world.

Citizens, both young and feeds, and semistructured metadata old, are also discovering how social media in the form of extensible markup language technology can improve their lives and (XML) and resource description give them more voice in the world. We they provide more useful, trustworthy, begin by describing an overarching task of and reliable. Pursuing this task uncovers It differs, however, in ways a number of problems that must be addressed, that affect how it should be modeled, analyzed, three of which we describe in and exploited. The first is recognizing spam model for the general web is as a directed graph of web pages with undifferentiated in the form of spam blogs (splogs) and links between pages. The second is developing has a much richer network structure more effective techniques to recognize in that there are more types of nodes the social structure of blog communities. For example, the abstract model for the underlying blog people who contribute to blogs and au-network structure and how it evolves. Figure 2 shows a hypothetical blog graph and its corresponding flow of information in the influence graph. Studies on influence in social networks and collaboration graphs have typically focused on the task of identifying key individuals who play an important role in propagating information. This is similar to finding authoritative pages on the web.