Summary of the paper
| Title | Characterizing and Predicting Bursty Events: the Buzz Case Study on Twitter |
|---|---|
| Authors | Mohamed Morchid, Georges Linares and Richard Dufour |
| 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. |
| Topics | Document Classification, Text categorisation, Other |
Full paper  |
Characterizing and Predicting Bursty Events: the Buzz Case Study on Twitter |
| Bibtex | @InProceedings{MORCHID14.19, author = {Mohamed Morchid and Georges Linares and Richard Dufour}, title = {Characterizing and Predicting Bursty Events: the Buzz Case Study on Twitter}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |