  First Story Detection describes the task of identifying new events in a
stream of documents. The UMass-FSD system is known for its strong performance
in First Story Detection competitions. Recently, it has been frequently used as
a high accuracy baseline in research publications. We are the first to discover
that UMass-FSD inadvertently leverages temporal bias. Interestingly, the
discovered bias contrasts previously known biases and performs significantly
better. Our analysis reveals an increased contribution of temporally distant
documents, resulting from an unusual way of handling incremental term
statistics. We show that this form of temporal bias is also applicable to other
well-known First Story Detection systems, where it improves the detection
accuracy. To provide a more generalizable conclusion and demonstrate that the
observed bias is not only an artefact of a particular implementation, we
present a model that intentionally leverages a bias on temporal distance. Our
model significantly improves the detection effectiveness of state-of-the-art
First Story Detection systems.
