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Conference papers

Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter

Abstract : The prediction of bursty events on the Internet is a challenging task. Difficulties are due to the diversity of information sources, the size of the Internet, dynamics of popularity, user behaviors.. . On the other hand, Twitter is a structured and limited space. In this paper, we present a new method for predicting bursty events using content-related indices. Prediction is performed by a neural network that combines three features in order to predict the number of retweets of a tweet on the Twitter platform. The indices are related to popularity, expressivity and singularity. Popularity index is based on the analysis of RSS streams. Expressivity uses a dictionary that contains words annotated in terms of expressivity load. Singularity represents outlying topic association estimated via a Latent Dirichlet Allocation (LDA) model. Experiments demonstrate the effectiveness of the proposal with a 72% F-measure prediction score for the tweets that have been forwarded at least 60 times.
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Submitted on : Monday, May 23, 2016 - 9:35:48 AM
Last modification on : Tuesday, March 22, 2022 - 2:40:01 PM


  • HAL Id : hal-01319806, version 1



Mohamed Morchid, Georges Linarès, Richard Dufour. Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter. LREC, May 2014, Reykjavik, Iceland. ⟨hal-01319806⟩



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