Name
Abstract | ||
|---|---|---|
Predicting the popularity of news articles-whether measured via retweets, clicks, or views-is an important problem for editors, journalists, and readers alike. In this paper, we introduce a new model to predict the shape of news article views, and use this model to determine when an article will likely reach its maximum number of views. Although volume prediction for news articles has been extensively studied predicting when a burst of views will happen, in what shape, and by how much, remains an open problem. We engineer several classes of features (metadata, contextual or content-based, temporal, and social), develop models to classify shape of views, with particular attention paid to performing online, time-updated, prediction, i.e., using data before and during the early stages of article prediction to predict its eventual peak views and update earlier predictions. The system presented here is an emerging application being developed at The Washington Post and can be used to support article placement, updating, and promotion strategies. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Peak Prediction, Shape Prediction, Time Series |
Field | DocType | Citations |
Data science,Metadata,Data mining,Time series,Open problem,Computer science,Popularity,Feature extraction,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaser Keneshloo | 1 | 27 | 2.99 |
| Shu-Guang Wang | 2 | 6 | 1.79 |
| Eui-hong Han | 3 | 0 | 0.34 |
| Naren Ramakrishnan | 4 | 1913 | 176.25 |