TY - JOUR
T1 - Controlling Fake News by Collective Tagging
T2 - A Branching Process Analysis
AU - Kapsikar, Suyog
AU - Saha, Indrajit
AU - Agarwal, Khushboo
AU - Kavitha, Veeraruna
AU - Zhu, Quanyan
N1 - Funding Information:
Manuscript received September 14, 2020; revised November 16, 2020; accepted December 3, 2020. Date of publication December 16, 2020; date of current version April 9, 2021. This work was supported by National Science Foundation (NSF) under Grant CNS-2027884, Grant SES-1541164, and Grant ECCS-1847056. Recommended by Senior Editor J.-F. Zhang. (Corresponding author: Khushboo Agarwal.) Suyog Kapsikar, Indrajit Saha, Khushboo Agarwal, and Veeraruna Kavitha are with the IEOR, Indian Institute of Technology Bombay, Mumbai 400076, India (e-mail: [email protected]).
Publisher Copyright:
© 2017 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - The spread of fake news on online social networks (OSNs) has become a matter of concern. These platforms are also used for propagating important authentic information. Thus, there is a need for mitigating fake news without significantly influencing the spread of real news. We leverage users' inherent capabilities of identifying fake news and propose a warning-based control mechanism to curb this spread. Warnings are based on previous users' responses that indicate the authenticity of the news. We use population-size dependent continuous-time multi-type branching processes to describe the spreading under the warning mechanism. We also have new results towards these branching processes. The (time) asymptotic proportions of the individual populations are derived using stochastic approximation tools. Using these, relevant type 1, type 2 performances are derived and an appropriate optimization problem is solved. The proposed mechanism effectively controls fake news, with negligible influence on the propagation of authentic news. We validate performance measures using Monte Carlo simulations on network connections provided by Twitter data.
AB - The spread of fake news on online social networks (OSNs) has become a matter of concern. These platforms are also used for propagating important authentic information. Thus, there is a need for mitigating fake news without significantly influencing the spread of real news. We leverage users' inherent capabilities of identifying fake news and propose a warning-based control mechanism to curb this spread. Warnings are based on previous users' responses that indicate the authenticity of the news. We use population-size dependent continuous-time multi-type branching processes to describe the spreading under the warning mechanism. We also have new results towards these branching processes. The (time) asymptotic proportions of the individual populations are derived using stochastic approximation tools. Using these, relevant type 1, type 2 performances are derived and an appropriate optimization problem is solved. The proposed mechanism effectively controls fake news, with negligible influence on the propagation of authentic news. We validate performance measures using Monte Carlo simulations on network connections provided by Twitter data.
KW - Fake news
KW - branching process
KW - stochastic approximation
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U2 - 10.1109/LCSYS.2020.3045299
DO - 10.1109/LCSYS.2020.3045299
M3 - Article
AN - SCOPUS:85098758150
SN - 2475-1456
VL - 5
SP - 2108
EP - 2113
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
IS - 6
M1 - 9296289
ER -