Predicting and Characterising User Impact on Twitter

The open structure of online social net-works and their uncurated nature give rise to problems of user credibility and influ-ence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets. We approach the problem as regression and apply linear as well as non-linear learning methods to predict a user impact score, estimated by combining the numbers of the user’s followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling com-ponents, transforming them into indirect ‘suggestions ’ for impact boosting.