@inproceedings{a6abdbd0b38d46fa9d9ada51ca643a39,
title = "Modeling disinformation and the effort to counter it: A cautionary tale of when the treatment can be worse than the disease",
abstract = "The problem of disinformation in online social networks has recently received a considerable amount of attention from the research community. It has been shown that online social networks are extensively getting exploited to alter public opinion and individuals' stance on a wide-range of topics. This study proposes an agent-based model that simulates a disinformation campaign by a group of organized users called conspirators, targeting a susceptible population, which are then opposed by a parallel organized group of users referred to as inouclators that try to act as a barrier to the spread of disinformation. The results of this study indicate that the process of inoculating a susceptible population against disinformation is mostly at the price of further polarizing the population.",
keywords = "Agent-based modeling, Disinformation, Misinformation, Polarization, Social media",
author = "Amirarsalan Rajabi and Chathika Gunaratne and Mantzaris, {Alexander V.} and Ivan Garibay",
note = "Publisher Copyright: {\textcopyright} 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.; 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 ; Conference date: 19-05-2020",
year = "2020",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "1975--1977",
editor = "Bo An and {El Fallah Seghrouchni}, Amal and Gita Sukthankar",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020",
}