Twitter’s big hitters

Peter Laflin; Alexander Vassilios Mantzaris; Desmond J. Higham; Peter Grindrod; Fiona Ainley; Amanda Otley

Twitter’s big hitters

Peter Laflin, Alexander Vassilios Mantzaris, Desmond J. Higham, Peter Grindrod, Fiona Ainley, Amanda Otley

Mathematics And Statistics

Research output: Contribution to conference › Paper › peer-review

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Abstract

We describe the results of a new computational experiment on Twitter data. By listening to Tweets on a selected topic, we generate a dynamic social interaction network. We then apply a recently proposed dynamic network analysis algorithm that ranks Tweeters according to their ability to broadcast information. In particular, we study the evolution of importance rankings over time. Our presentation will also describe the outcome of an experiment where results from automated ranking algorithms are compared with the views of social media experts.

Original language	English
Number of pages	3
Publication status	Published - 23 Nov 2012
Event	Digital Futures 2012 - Aberdeen, United Kingdom Duration: 23 Oct 2012 → 25 Oct 2012

Conference

Conference	Digital Futures 2012
Country	United Kingdom
City	Aberdeen
Period	23/10/12 → 25/10/12

Keywords

twitter
big hitters
centrality
social network analysis
dynamic network

Access to Document

DE2012TwitterSubmitted manuscript, 312 KB

http://www.de2012.org/

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Commercial advantage through computational discovery of dynamic communicators in large digital networks
Desmond Higham (Participant), Ernesto Estrada (Participant) & Peter Grindrod (Participant)
Impact: Impact - for External Portal › Economic and commerce

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Cite this

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AU - Laflin, Peter

AU - Mantzaris, Alexander Vassilios

AU - Higham, Desmond J.

AU - Grindrod, Peter

AU - Ainley, Fiona

AU - Otley, Amanda

PY - 2012/11/23

Y1 - 2012/11/23

N2 - We describe the results of a new computational experiment on Twitter data. By listening to Tweets on a selected topic, we generate a dynamic social interaction network. We then apply a recently proposed dynamic network analysis algorithm that ranks Tweeters according to their ability to broadcast information. In particular, we study the evolution of importance rankings over time. Our presentation will also describe the outcome of an experiment where results from automated ranking algorithms are compared with the views of social media experts.

AB - We describe the results of a new computational experiment on Twitter data. By listening to Tweets on a selected topic, we generate a dynamic social interaction network. We then apply a recently proposed dynamic network analysis algorithm that ranks Tweeters according to their ability to broadcast information. In particular, we study the evolution of importance rankings over time. Our presentation will also describe the outcome of an experiment where results from automated ranking algorithms are compared with the views of social media experts.

KW - twitter

KW - big hitters

KW - centrality

KW - social network analysis

KW - dynamic network

UR - http://www.de2012.org/

M3 - Paper

T2 - Digital Futures 2012

Y2 - 23 October 2012 through 25 October 2012

ER -

Twitter’s big hitters

Abstract

Conference

Keywords

Access to Document

Commercial advantage through computational discovery of dynamic communicators in large digital networks

Cite this