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Understanding human-machine networks: A cross-disciplinary survey

Understanding human-machine networks: A cross-disciplinary survey
Understanding human-machine networks: A cross-disciplinary survey
In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.
0360-0300
Tsvetkova, Milena
8c1178b4-625f-4495-8ad2-aa443d4f07f4
Yasseri, Taha
f80b4fe6-05ff-4a57-8167-e788b57b74c4
Meyer, Eric
52c7ac3b-632b-44fb-a53a-ca834aae421f
Pickering, Brian
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Engen, Vegard
5ab4f73a-6cb5-4a58-9d89-ebced3182962
Walland, Paul
ee411ac1-9ebc-4513-a691-a3b95b599d7f
Luders, Marika
da168177-a5a4-48b1-8037-ebc49b1eb914
Folstad, Asbjorn
bb1a064c-e005-436b-878c-6bd6e41c172e
Bravos, George
3e9aac2e-1a72-4f47-aaee-5295d55a0432
Tsvetkova, Milena
8c1178b4-625f-4495-8ad2-aa443d4f07f4
Yasseri, Taha
f80b4fe6-05ff-4a57-8167-e788b57b74c4
Meyer, Eric
52c7ac3b-632b-44fb-a53a-ca834aae421f
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Engen, Vegard
5ab4f73a-6cb5-4a58-9d89-ebced3182962
Walland, Paul
ee411ac1-9ebc-4513-a691-a3b95b599d7f
Luders, Marika
da168177-a5a4-48b1-8037-ebc49b1eb914
Folstad, Asbjorn
bb1a064c-e005-436b-878c-6bd6e41c172e
Bravos, George
3e9aac2e-1a72-4f47-aaee-5295d55a0432

Tsvetkova, Milena, Yasseri, Taha, Meyer, Eric, Pickering, Brian, Engen, Vegard, Walland, Paul, Luders, Marika, Folstad, Asbjorn and Bravos, George (2017) Understanding human-machine networks: A cross-disciplinary survey. ACM Computing Surveys, 50 (1). (doi:10.1145/3039868).

Record type: Article

Abstract

In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

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Accepted/In Press date: 11 January 2017
e-pub ahead of print date: 13 April 2017
Published date: April 2017

Identifiers

Local EPrints ID: 414459
URI: http://eprints.soton.ac.uk/id/eprint/414459
ISSN: 0360-0300
PURE UUID: 0dce36d8-88ae-48d7-8fc0-463689834dec
ORCID for Brian Pickering: ORCID iD orcid.org/0000-0002-6815-2938

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Date deposited: 29 Sep 2017 16:31
Last modified: 07 Oct 2020 01:58

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