I would like to have some advice on the way to calculate burstiness. I am working with a set of text data, where every term is calculated with their frequency in newspaper for 2 weeks, e.g. "apple" during their iphone4s release will be day1 = 10, day2 = 300, day3 = 25, and so on till day14. So if popular term such as Obama which appear day1 = 100, day2=100, day3=105 ... since it is popular but some other terms which is bursty and spiky will be like "apple" example.
Is there any way that we can measure such burstiness, I am aware of standard deviation, and is there any other ways? The ultimate goal is to make burstiness(Obama) as small as possible and the burstiness(apple) as high as possible. Thanks.