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10.1007/s10796-020-10102-8 http://scihub22266oqcxt.onion/10.1007/s10796-020-10102-8 33613087!7880039!33613087     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inf+Syst+Front 2021 ; 23 (5): 1169-1188 Nephropedia Template TP {{tp|p=33613087|t=2021. Learning Automata-based Misinformation Mitigation via Hawkes Processes |pdf=|usr=}}{{33613087}} gab.com Text Learning Automata-based Misinformation Mitigation via Hawkes Processes JO: Inf Syst Front http://www.kidney.de/pget.php?&tw=1&pmid=33613087 #FOAvip Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios. Twit Text FOAVip Learning Automata-based Misinformation Mitigation via Hawkes Processes ????Inf Syst Front http://www.kidney.de/pget.php?&tw=1&pmid=33613087 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33613087 #FOAvip English Wikipedia <ref>{{Cite pmid|33613087}}</ref> Learning Automata-based Misinformation Mitigation via Hawkes Processes #MMPMID33613087 Abouzeid A; Granmo OC; Webersik C; Goodwin M Inf Syst Front 2021[]; 23 (5): 1169-1188 PMID33613087show ga Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint random walk over the state space. We use three Twitter datasets to evaluate our approach, one of them being a new COVID-19 dataset provided in this paper. Our approach shows fast convergence and increased valid information exposure. These results persisted independently of network structure, including networks with central nodes, where the latter could be the root of misinformation. Further, the LA obtained these results in a decentralized manner, facilitating distributed deployment in real-life scenarios. äLearning Automata-based Misinformation Mitigation via Hawkes Processes(epmc).pdf DeepDyve Pubget Overpricing