In this paper, we propose the beginnings of a formal framework for modeling narrative \textit{qua} narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader and two novel measurements of story coherence.

We are excited to announce that, to help us get there, we have acquired Fabula AI (Fabula), a London-based start-up, with a world-class team of machine learning researchers who employ graph deep learning to detect network manipulation. Graph deep learning is a novel method for applying powerful ML techniques to network-structured data. The result is the ability to analyze very large and complex datasets describing relations and interactions, and to extract signals in ways that traditional ML techniques are not capable of doing. Twitter has been criticized for the amount of fake news and misinformation that easily spreads on its platform. Though the company has taken steps to combat such misinformation in recent years, fake news is still a major problem for the social network.

Twitter has just announced it has picked up London-based Fabula AI. The deep learning startup has been developing technology to try to identify online disinformation by looking at patterns in how fake stuff vs genuine news spreads online -- making it an obvious fit for the rumor-riled social network. Social media giants remain under increasing political pressure to get a handle on online disinformation to ensure that manipulative messages don't, for example, get a free pass to fiddle with democratic processes. Twitter says the acquisition of Fabula will help it build out its internal machine learning capabilities -- writing that the UK startup's "world-class team of machine learning researchers" will feed an internal research group it's building out, led by Sandeep Pandey, its head of ML/AI engineering. This research group will focus on "a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning" -- now with Fabula co-founder and chief scientist, Michael Bronstein, as a leading light within it.

UK startup Fabula AI reckons it's devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals. Even Facebook's Mark Zuckerberg has sounded a cautious note about AI technology's capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side. "It will take many years to fully develop these systems," the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. "This is technically difficult as it requires building AI that can read and understand news." But what if AI doesn't need to read and understand news in order to detect whether it's true or false? Step forward Fabula, which has patented what it dubs a "new class" of machine learning algorithms to detect "fake news" -- in the emergent field of "Geometric Deep Learning"; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this'non-Euclidean' space. The startup says its deep learning algorithms are, by contrast, capable of learning patterns on complex, distributed data sets like social networks.

In this paper we propose an ontological framework for tools facilitating creative writing and story reading. It is based on an ontology implemented as a topic map and employs linguistic analysis methods for discovering conceptual entities in the text.

In this paper we propose a middleware to reuse multimedia resources in order to produce new types of multimedia artifacts. In this work we adopt some basic concepts of film theory, such as the notions of plot, fabula and, in particular, diegetic time. The techniques we use are located within the area of artificial intelligence, using an explicit representation of time. The middleware consists of several modules, some devoted to the semantic annotation of multimedia components, and others to their visualization. Some modules regard the analysis of temporal connectivity and consistency of events. From a methodological point of view, an important module of the middleware contains the representation of a story (time of the narration and time of the story) and the temporal reasoning services, which are both implemented using a logic programming language (Flora2). Finally, there is a module in the middleware that translates the logical representation (in Flora2 language) into SMIL language, which allows the use of the final composition by a standard player.