  Today's social networks continuously generate massive streams of data, which
provide a valuable starting point for the detection of rumours as soon as they
start to propagate. However, rumour detection faces tight latency bounds, which
cannot be met by contemporary algorithms, given the sheer volume of
high-velocity streaming data emitted by social networks. Hence, in this paper,
we argue for best-effort rumour detection that detects most rumours quickly
rather than all rumours with a high delay. To this end, we combine techniques
for efficient, graph-based matching of rumour patterns with effective load
shedding that discards some of the input data while minimising the loss in
accuracy. Experiments with large-scale real-world datasets illustrate the
robustness of our approach in terms of runtime performance and detection
accuracy under diverse streaming conditions.
