Final published version, 1.12 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Birds of a feather talk together
T2 - user influence on language adoption
AU - Kershaw, Daniel
AU - Rowe, Matthew Charles
AU - Stacey, Patrick Keith
AU - Noulas, Anastasios
PY - 2017/1/8
Y1 - 2017/1/8
N2 - Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusions of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures.
AB - Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusions of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures.
U2 - 10.24251/HICSS.2017.225
DO - 10.24251/HICSS.2017.225
M3 - Conference contribution/Paper
SN - 9780998133102
SP - 1851
EP - 1860
BT - Proceedings of the 50th Hawaii International Conference on System Sciences
PB - IEEE
ER -