  The propagation of rumours on social media poses an important threat to
societies, so that various techniques for rumour detection have been proposed
recently. Yet, existing work focuses on \emph{what} entities constitute a
rumour, but provides little support to understand \emph{why} the entities have
been classified as such. This prevents an effective evaluation of the detected
rumours as well as the design of countermeasures. In this work, we argue that
explanations for detected rumours may be given in terms of examples of related
rumours detected in the past. A diverse set of similar rumours helps users to
generalize, i.e., to understand the properties that govern the detection of
rumours. Since the spread of rumours in social media is commonly modelled using
feature-annotated graphs, we propose a query-by-example approach that, given a
rumour graph, extracts the $k$ most similar and diverse subgraphs from past
rumours. The challenge is that all of the computations require fast assessment
of similarities between graphs. To achieve an efficient and adaptive
realization of the approach in a streaming setting, we present a novel graph
representation learning technique and report on implementation considerations.
Our evaluation experiments show that our approach outperforms baseline
techniques in delivering meaningful explanations for various rumour propagation
behaviours.
