Social Reinforcement Learning to Combat Fake News Spread

Mahak Goindani, Jennifer Neville
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:1006-1016, 2020.

Abstract

In this work, we develop a social reinforcement learning approach to combat the spread of fake news. Specifically, we aim to learn an intervention model to promote the spread of true news in a social network—in order to mitigate the impact of fake news. We model news diffusion as a Multivariate Hawkes Process (MHP) and make interventions that are learnt via policy optimization. The key insight is to estimate the response a user will get from the social network upon sharing a post, as it indicates her impact on diffusion, and will thus help in efficient allocation of incentive. User responses also depend on political bias and peer influence, which we model as a second MHP, interleaving it with the news diffusion process. We evaluate our model on semi-synthetic and real-world data. The results demonstrate that our proposed model outperforms other alternatives that do not consider estimates of user responses and political bias when learning how to allocate incentives.

Cite this Paper

BibTeX
@InProceedings{pmlr-v115-goindani20a, title = {Social Reinforcement Learning to Combat Fake News Spread}, author = {Goindani, Mahak and Neville, Jennifer}, booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, pages = {1006--1016}, year = {2020}, editor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, series = {Proceedings of Machine Learning Research}, month = {22--25 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v115/goindani20a/goindani20a.pdf}, url = {https://proceedings.mlr.press/v115/goindani20a.html}, abstract = {In this work, we develop a social reinforcement learning approach to combat the spread of fake news. Specifically, we aim to learn an intervention model to promote the spread of true news in a social network—in order to mitigate the impact of fake news. We model news diffusion as a Multivariate Hawkes Process (MHP) and make interventions that are learnt via policy optimization. The key insight is to estimate the response a user will get from the social network upon sharing a post, as it indicates her impact on diffusion, and will thus help in efficient allocation of incentive. User responses also depend on political bias and peer influence, which we model as a second MHP, interleaving it with the news diffusion process. We evaluate our model on semi-synthetic and real-world data. The results demonstrate that our proposed model outperforms other alternatives that do not consider estimates of user responses and political bias when learning how to allocate incentives.} }
Endnote
%0 Conference Paper %T Social Reinforcement Learning to Combat Fake News Spread %A Mahak Goindani %A Jennifer Neville %B Proceedings of The 35th Uncertainty in Artificial Intelligence Conference %C Proceedings of Machine Learning Research %D 2020 %E Ryan P. Adams %E Vibhav Gogate %F pmlr-v115-goindani20a %I PMLR %P 1006--1016 %U https://proceedings.mlr.press/v115/goindani20a.html %V 115 %X In this work, we develop a social reinforcement learning approach to combat the spread of fake news. Specifically, we aim to learn an intervention model to promote the spread of true news in a social network—in order to mitigate the impact of fake news. We model news diffusion as a Multivariate Hawkes Process (MHP) and make interventions that are learnt via policy optimization. The key insight is to estimate the response a user will get from the social network upon sharing a post, as it indicates her impact on diffusion, and will thus help in efficient allocation of incentive. User responses also depend on political bias and peer influence, which we model as a second MHP, interleaving it with the news diffusion process. We evaluate our model on semi-synthetic and real-world data. The results demonstrate that our proposed model outperforms other alternatives that do not consider estimates of user responses and political bias when learning how to allocate incentives.
APA
Goindani, M. & Neville, J.. (2020). Social Reinforcement Learning to Combat Fake News Spread. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:1006-1016 Available from https://proceedings.mlr.press/v115/goindani20a.html.

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