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.- Anthology ID:
- L14-1196
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2766–2771
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/19_Paper.pdf
- DOI:
- Cite (ACL):
- Mohamed Morchid, Georges Linarès, and Richard Dufour. 2014. Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2766–2771, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter (Morchid et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/19_Paper.pdf