research-article

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

Authors:

Ludovic Denoyer,

Patrick Gallinari,

Peter DologAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 620 - 627

https://doi.org/10.1145/2492517.2492541

Published: 25 August 2013 Publication History

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.

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Cited By

Kar ASingh RLiu FLiu XShroff N(2024)Linear Bandits With Side Observations on NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2024.342232332:5(4222-4237)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422323
Odeyomi O(2020)Learning the Truth in Social Networks Using Multi-Armed BanditIEEE Access10.1109/ACCESS.2020.30125938(137692-137701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3012593
Lamprier SGisselbrecht TGallinari P(2019)Contextual bandits with hidden contexts: a focused data capture from social media streamsData Mining and Knowledge Discovery10.1007/s10618-019-00648-wOnline publication date: 10-Aug-2019
https://doi.org/10.1007/s10618-019-00648-w

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

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2013

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ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Kar ASingh RLiu FLiu XShroff N(2024)Linear Bandits With Side Observations on NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2024.342232332:5(4222-4237)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3422323
Odeyomi O(2020)Learning the Truth in Social Networks Using Multi-Armed BanditIEEE Access10.1109/ACCESS.2020.30125938(137692-137701)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3012593
Lamprier SGisselbrecht TGallinari P(2019)Contextual bandits with hidden contexts: a focused data capture from social media streamsData Mining and Knowledge Discovery10.1007/s10618-019-00648-wOnline publication date: 10-Aug-2019
https://doi.org/10.1007/s10618-019-00648-w
Gisselbrecht TLamprier SGallinari P(2015)Policies for Contextual Bandit Problems with Count PayoffsProceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2015.85(542-549)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/ICTAI.2015.85

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