Analyzing and Predicting News Popularity in an Instant Messaging Service

Abstract

With widespread use of mobile devices, instant messaging (IM) services have recently attracted a great deal of attention by millions of users. This has motivated news agencies to share their contents via such platforms in addition to their websites and popular social media. As a result, thousands of users nowadays follow the news agencies through their verified channels in IM services. However, user interactions with such platforms is relatively unstudied. In this paper, we provide an initial study to analyze and predict news popularity in an instant messaging service. To this aim, we focus on Telegram, a popular IM service with 200 million monthly active users. We explore the differences between news popularity analysis in Telegram and typical social media, such as Twitter, and highlight its unique characteristics. We perform our analysis on the data we collected from four diverse news agencies. Following our analysis, we study the task of news popularity prediction in Telegram and show that the performance of the prediction models can be substantially improved by learning from the data of multiple news agencies using multi-task learning. To foster research in this area, we have made the collected data publicly available.