Choosing which message to publish on social networks: A contextual bandit approach

Maximizing the spread and influence of the messages being published is a challenge for many social network users. Selecting the right content according to the information context and the user characteristics is essential for achieving this goal. We propose a model to automatically choose which information to publish on social networks given a set of possible messages. This model will tend to maximize the spread of the published message for a specific audience. The algorithm is based on the use of a contextual bandit model treating each new potential message as an arm to be selected. We conduct experiments on a Twitter dataset, comparing different algorithms and exploring the influence of the content and the characteristics of the messages on the information spread. The results demonstrate the model´s ability to maximize the published information flow as well as it´s ability to adapt its behavior to each particular audience.