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Designing Social Machines for Tackling Online Disinformation

Type
book section
Author(s)
Wild, Antonia  
;
Ciortea, Andrei  
;
Mayer, Simon  
Abstract (De)
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.
Language
English
HSG Classification
contribution to scientific community
Book title
Companion Proceedings of The World Wide Web Conference (WWW 2020)
Event Title
A Decentralised Web Workshop 2020
Event Location
Taipeh, TW
Event Date
20.04.2020
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/116714
Subject(s)

computer science

Division(s)

ICS - Institute of Co...

ISCM - Institute of S...

Eprints ID
259529
File(s)
Thumbnail Image
Name

Wild-Ciortea-Mayer-SocialMachinesOnlineDisinformation-2020.pdf

Size

1.79 MB

Format

Adobe PDF

Checksum (MD5)

9379d03c0c8bbaea24c627bfa7be02b9

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