Fake News Mitigation via Point Process Based Intervention

Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1097-1106, 2017.

Abstract

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-farajtabar17a, title = {Fake News Mitigation via Point Process Based Intervention}, author = {Mehrdad Farajtabar and Jiachen Yang and Xiaojing Ye and Huan Xu and Rakshit Trivedi and Elias Khalil and Shuang Li and Le Song and Hongyuan Zha}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {1097--1106}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/farajtabar17a/farajtabar17a.pdf}, url = {https://proceedings.mlr.press/v70/farajtabar17a.html}, abstract = {We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.} }
Endnote
%0 Conference Paper %T Fake News Mitigation via Point Process Based Intervention %A Mehrdad Farajtabar %A Jiachen Yang %A Xiaojing Ye %A Huan Xu %A Rakshit Trivedi %A Elias Khalil %A Shuang Li %A Le Song %A Hongyuan Zha %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-farajtabar17a %I PMLR %P 1097--1106 %U https://proceedings.mlr.press/v70/farajtabar17a.html %V 70 %X We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets.
APA
Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L. & Zha, H.. (2017). Fake News Mitigation via Point Process Based Intervention. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1097-1106 Available from https://proceedings.mlr.press/v70/farajtabar17a.html.

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