Who should fight the spread of fake news?

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Diana Riazi , Giacomo Livan
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引用次数: 0

Abstract

This study investigates who should bear the responsibility of combating the spread of misinformation in social networks. Should that be the online platforms or their users? Should that be done by debunking the ‘fake news’ already in circulation or by investing in preemptive efforts to prevent their diffusion altogether? We seek to answer such questions in a stylized opinion dynamics framework, where agents in a network aggregate the information they receive from peers and/or from influential external sources, with the aim of learning a ground-truth among a set of competing hypotheses. In most cases, we find centralized sources to be more effective at combating misinformation than distributed ones, suggesting that online platforms should play an active role in the fight against fake news. In line with literature on the ‘backfire effect’, we find that debunking in certain circumstances can be a counterproductive strategy, whereas some targeted strategies (akin to ‘deplatforming’) and/or preemptive campaigns turn out to be quite effective. Despite its simplicity, our model provides useful guidelines that could inform the ongoing debate on online disinformation and the best ways to limit its damaging effects.
谁应该打击假新闻的传播?
这项研究调查了谁应该承担打击社交网络中错误信息传播的责任。应该是在线平台还是他们的用户?是通过揭穿已经在流通的“假新闻”,还是通过先发制人的努力来阻止它们的传播来做到这一点?我们试图在一个程式化的意见动态框架中回答这些问题,在这个框架中,网络中的代理汇总他们从同行和/或有影响力的外部来源获得的信息,目的是在一组相互竞争的假设中学习基本真相。在大多数情况下,我们发现集中式来源在打击错误信息方面比分布式来源更有效,这表明在线平台应该在打击假新闻方面发挥积极作用。根据关于“适得其反的效果”的文献,我们发现在某些情况下揭穿可能是一种适得其反的策略,而一些有针对性的策略(如“去平台化”)和/或先发制人的活动却非常有效。尽管它很简单,但我们的模型提供了有用的指导方针,可以为正在进行的关于在线虚假信息的辩论提供信息,并提供限制其破坏性影响的最佳方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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