A framework to predict early news popularity using deep temporal propagation patterns

The increasing competition among the news industries puts editors under the pressure of posting news articles that should gain more user attention. News popularity is predicted using different content and metadata features. Some approaches use retweet paths formed on social media when a tweet is retweeted. However, before a piece of news spreads by retweeting, there are several initial tweets made by multiple different users that spread the same news. Retweeting behavior serves as the secondary features in this case while the initial tweets serve as the primary features. In this work, the popularity of a news item published on a certain website is predicted by exploiting the initial tweeting behavior of the news item on Twitter. The temporal characteristics of a news item are exploited as the news propagates via tweets. Additionally, other content and metadata features have also been used to predict news popularity. Data is extracted from different websites of cybersecurity news and Twitter. A deep neural network is proposed to predict early news popularity. The proposed model yields the macro averaged F-score of 92% which shows the effectiveness of temporal propagation patterns in predicting news popularity. The proposed model is compared with the baseline models and state-of-the-art techniques, and it is shown that the proposed model outperforms all the existing techniques.

Automated summary

This study predicts the popularity of news articles on the internet by using the initial tweeting behavior and temporal characteristics. A deep neural network model is proposed, which outperforms existing techniques in predicting news popularity.