Using the profile of publishers to predict barriers across news articles
This work addresses the open issue of identifying barriers in news spreading, which is incremental as it applies existing methods to a new domain-specific dataset.
The paper tackled the problem of detecting news propagation barriers by using Wikipedia-concepts and metadata, achieving high accuracy with simple classification models on the IPoNews dataset.
Detection of news propagation barriers, being economical, cultural, political, time zonal, or geographical, is still an open research issue. We present an approach to barrier detection in news spreading by utilizing Wikipedia-concepts and metadata associated with each barrier. Solving this problem can not only convey the information about the coverage of an event but it can also show whether an event has been able to cross a specific barrier or not. Experimental results on IPoNews dataset (dataset for information spreading over the news) reveals that simple classification models are able to detect barriers with high accuracy. We believe that our approach can serve to provide useful insights which pave the way for the future development of a system for predicting information spreading barriers over the news.