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

Ricardo Lage , Ludovic Denoyer (1) , Patrick Gallinari (1) , Peter Dolog

1 MLIA - Machine Learning and Information Access

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.

Domaines: Informatique [cs]

Ricardo Lage, Ludovic Denoyer, Patrick Gallinari, Peter Dolog. Choosing which message to publish on social networks: A Contextual bandit approach. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Aug 2013, Niagara Falls, Ontario, Canada. pp.620-627. ⟨hal-01215178⟩