User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.
News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of content popularity prediction and has direct implications in various contexts such as caching strategies or online advertisement policies. In this paper we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task into a ranking problem where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. To this end, we analyze the ranking performance of three prediction models using a dataset of articles covering a four-year period and published by 20minutes.fr, an important French online news platform. Our results indicate that prediction methods improve the ranking performance and we observed that for our dataset a simple linear prediction method outperforms more dedicated prediction methods.