News Patterns: how press interacts with social networks

Abstract

Social media has played a big part in the adaptation process for newspapers and magazines, but innovating while going through a recession has led to a hasty evolution and automated processes for very different media. While existing socialmedia studies and state of the art visual solutions are available for analyzing social media content and users’ behaviors, no other method is optimized for finding patterns from a popularity standpoint in the specialized realm of news channels. In this paper, we propose the usage of a combination of different visualization techniques that co-relate the profile’s and its reading community activities with the resulting popularity. For the period of three months, we gathered Twitter posts, the number of followers and trending topics from worldwide press profiles. We used this dataset as the seed for our bar charts, tag clouds and bubble charts to allow for multiple source comparison, so that not only the user is able to understand their own community but also the success and pitfalls faced by the competition in the same medium. We validate our analysis by interviewing a group of journalists from different established newspapers. Through interacting with our system, it was possible to reveal hidden patterns in the massive dataset of messages and comments worldwide enabling the user to have unique insight into their community’s behaviors and preferences.