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

Authors:
Ricardo Lage;Ludovic Denoyer;Patrick Gallinari;Peter Dolog
Affiliations:
Aalborg University, Denmark;University Pierre et Marie Curie (UPMC-CNRS);University Pierre et Marie Curie (UPMC-CNRS);Aalborg University, Denmark
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

Citing 14
Cited 0

Maximizing the spread of influence through a social network

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration

Information Systems Research
In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems

User Modeling and User-Adapted Interaction
Personalized recommendation on dynamic content using predictive bilinear models

Proceedings of the 18th international conference on World wide web
What is Twitter, a social network or a news media?

Proceedings of the 19th international conference on World wide web
A contextual-bandit approach to personalized news article recommendation

Proceedings of the 19th international conference on World wide web
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

Proceedings of the fourth ACM international conference on Web search and data mining
Threshold models for competitive influence in social networks

WINE'10 Proceedings of the 6th international conference on Internet and network economics
Who says what to whom on twitter

Proceedings of the 20th international conference on World wide web
Group recommendations via multi-armed bandits

Proceedings of the 21st international conference companion on World Wide Web
A predictive model for the temporal dynamics of information diffusion in online social networks

Proceedings of the 21st international conference companion on World Wide Web
Towards effective group recommendations for microblogging users

Proceedings of the 27th Annual ACM Symposium on Applied Computing
Collaborative personalized tweet recommendation

SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting information diffusion in social networks using content and user's profiles

ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.