Designing Social Machines for Tackling Online Disinformation

Item Type Book Section

Traditional news outlets as carriers and distributors of information have been challenged by online social networks with regards to their gate-keeping function. Users are now left with the difficult
task of assessing the credibility of information provided to them, which facilitates the spread of disinformation. At the same time, current human- and machine-based approaches to tackle disinformation are operating in isolation from one another, each with its respective weaknesses. We believe that only a combined effort of people and machines will be able to curb so-called "fake news" at scale in a decentralized Web. In this paper, we propose an approach to design social machines that coordinate human- and machinedriven credibility assessment of information on a decentralized Web. To this end, we defined a fact-checking process that draws upon ongoing efforts for tackling disinformation on the Web, and we formalized this process as a multi-agent organisation for curating W3C Web Annotations. We present the current state of our prototypical implementation in the form of a browser plugin that builds on the Hypothesis annotation platform and the JaCaMo multiagent platform. Our social machines can span across the Web to enable collaboration in form of public discourse, thereby increasing the transparency and accountability of information on the Web.

Authors Wild, Antonia; Ciortea, Andrei & Mayer, Simon
Language English
Subjects computer science
HSG Classification contribution to scientific community
Title of Book Companion Proceedings of The World Wide Web Conference (WWW 2020)
Event Title A Decentralised Web Workshop 2020
Event Location Taipeh, TW
Event Dates 20.04.2020
Depositing User Prof. Dr. Simon Mayer
Date Deposited 02 Mar 2020 09:03
Last Modified 11 May 2021 13:58


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Wild, Antonia; Ciortea, Andrei & Mayer, Simon: Designing Social Machines for Tackling Online Disinformation. In Companion Proceedings of The World Wide Web Conference (WWW 2020).

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