Paper Info
Title
On predicting the popularity of newly emerging hashtags in Twitter.
Abstract
Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i. e., Naive bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
Year
DOI
Venue
2013
10.1002/asi.22844
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
Keywords
Field
DocType
text mining,content filtering,automatic classification
Decision tree,Data mining,Content filtering,Social graph,Naive Bayes classifier,Information retrieval,Computer science,Popularity,Support vector machine,Information Dissemination,Logistic regression
Journal
Volume
Issue
ISSN
64
7
1532-2882
Citations 
PageRank 
References 
87
2.77
25
Authors
3
Name
Order
Citations
PageRank
Zongyang Ma151816.13
Aixin Sun23071156.89
gao cong34086169.93