If successful, our project should contribute to:
The analysis of fake news: understand an article about a controversial topic, and allow reasoning on it (who said what when and why, what is the evidence, how is the perception of the claims by others), with the goal to support journalists in the fact checking of a story.
The implementation of the “right to be forgotten”: analyze requests sent to search engines to help human reviewers in determining whether the requests meet legal criteria.
The modeling of controversies: detect a controversial topic on the Web (e.g., in blogs, forums, or Twitter posts), extract opinions, and model different standpoints.
The analysis of the e-reputation of a company (or its competitor): map out cases of controversy or beliefs of valuations, together with their reasons, and their support among journalists, clients, and the general public. This can include the analysis of product reviews to identify fine-grained praise or complaints.
The flagging of potentially fraudulent activity: detect patterns in textual communication that indicate fraud, claims that are in contradiction with established knowledge, or violations of rules.
The modeling of processes: Summaries of technical interventions often contain sequences of actions performed, causal relation- ships, and suggestions, which could be extracted and analyzed with our model.
The development of smarter chatbots: allow dialogues that go beyond single-shot questions, build up a mental model of the user and their beliefs, and reason on them. Several companies develop chatbots, and this field is thus a rich ground for applications.
