Abstract
In Twitter and in other social media channels, detecting events is very important and has many applications. However, this task is very challenging because of the huge number of tweets that are posted every minute and the massive scale of the spamming activities. In this paper, we present an innovative approach for detecting events using data posted to Twitter. The proposed approach is based on the concept of user's attention by quantitatively modelling the diversity of hashtags using Shannon's index. Our method records the diversity values on an hourly basis time-series. Using statistical techniques, the method locates the intervals having diversity values that fall outside the range of forecasted ones (normal state). We also present the labelling and ranking techniques that are implemented in this research. Experimental results on a dataset consisting of 15 million Arabic tweets show that our proposed approach can effectively detect real-world events in Twitter.
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