In such a dynamic setting, burst detection algorithms should effectively collect evidence, efficiently adjust prediction models and respond to the users as social media activities evolve.
to overcome this, baseline method StateMachine (g 1 + L = 2) requires larger ρ and γ 1 , which may discard relatively small meaningful bursts; while our proposed single stream method (g 2 + L = 3, 4) tends to identify steady and consecutive bursts through the help of longer context window and context sensitive smoothness function g 2 , it is more suitable to be applied to social media for burst detection.