False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

The automatic generation of narratives

We present the Narrator, a Natural Language Generation component used in a digital storytelling system. The system takes as input a formal representation of a story plot, in the form of a causal network relating the actions of the characters to their motives and their consequences. Based on this input, the Narrator generates a narrative in Dutch, by carrying out tasks such as constructing a Document Plan, performing aggregation and ellipsis and the generation of appropriate referring expressions. We describe how these tasks are performed and illustrate the process with examples, showing how this results in the generation of coherent and well-formed narrative texts