Name
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. |
Year | Venue | Keywords |
|---|---|---|
2014 | LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | Bursty events detection,Latent Dirichlet Allocation,Neural network |
Field | DocType | Citations |
Latent Dirichlet allocation,Computer science,Popularity,Singularity,Artificial intelligence,Natural language processing,Artificial neural network,The Internet,Expressivity,World Wide Web,Information retrieval,RSS,Marketing buzz | Conference | 1 |
PageRank | References | Authors |
0.36 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Morchid | 1 | 84 | 22.79 |
| Georges Linares | 2 | 87 | 19.73 |
| richard dufour | 3 | 98 | 23.98 |