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Indexing Evolving Events from Tweet Streams
Indexing Evolving Events from Tweet Streams
Abstract—:Tweet streams provide a variety of real-time information on dynamic social events. Although event detection has been actively studied, most of the existing approaches do not address the issue of efficient event monitoring in the presence of a large number of events detected from continuous tweet streams.
In this paper, we capture the dynamics of events using four event operations: creation, absorption, split and merge. We also propose a novel event indexing structure, named Multi-layer Inverted List (MIL), for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search
using MIL based on upper bound pruning. Extensive experiments have been conducted on a large-scale tweet dataset. The results demonstrate the promising performance of our method in terms of both efficiency and effectiveness.
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