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Measuring and Modeling Information F...

Pond, Tyson Charles.

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Measuring and Modeling Information Flow on Social Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Measuring and Modeling Information Flow on Social Networks./
Author:	Pond, Tyson Charles.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	82 p.
Notes:	Source: Masters Abstracts International, Volume: 81-10.
Contained By:	Masters Abstracts International81-10.
Subject:	Applied mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834367
ISBN:	9798607322588

Measuring and Modeling Information Flow on Social Networks.
Pond, Tyson Charles.

Measuring and Modeling Information Flow on Social Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 82 p.

Source: Masters Abstracts International, Volume: 81-10.

Thesis (M.S.)--The University of Vermont and State Agricultural College, 2020.

This item must not be sold to any third party vendors.

With the rise of social media, researchers have become increasingly interested in understanding how individuals inform, influence, and interact with others in their social network and how the network mediates the flow of information. Previous research on information flow has primarily used models of contagion to study the adoption of a technology, propagation of purchase recommendations, or virality of online activity. Social (or "complex") contagions spread differently than biological ("simple") contagions. A limitation when researchers validate contagion models is that they neglect much of the massive amounts of data now available through online social networks. Here we model a recently proposed information-theoretic approach to measuring the flow of written information in data. We use an idealized generative model for text data -- the quoter model -- which naturally incorporates this measure. We investigate how network structure impacts information flow and find that the quoter model exhibits interesting features similar to those of complex contagion. Finally, we offer an analytical treatment of the quoter model: we derive approximate calculations and show dependence on model parameters. This thesis gives rise to new hypotheses about the role of the social network in facilitating information flow, which future research can investigate using real-world data.

ISBN: 9798607322588Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Complex systems

Measuring and Modeling Information Flow on Social Networks.
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245 1 0 $a Measuring and Modeling Information Flow on Social Networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 82 p.
500 $a Source: Masters Abstracts International, Volume: 81-10.
500 $a Advisor: Bagrow, James P.
502 $a Thesis (M.S.)--The University of Vermont and State Agricultural College, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a With the rise of social media, researchers have become increasingly interested in understanding how individuals inform, influence, and interact with others in their social network and how the network mediates the flow of information. Previous research on information flow has primarily used models of contagion to study the adoption of a technology, propagation of purchase recommendations, or virality of online activity. Social (or "complex") contagions spread differently than biological ("simple") contagions. A limitation when researchers validate contagion models is that they neglect much of the massive amounts of data now available through online social networks. Here we model a recently proposed information-theoretic approach to measuring the flow of written information in data. We use an idealized generative model for text data -- the quoter model -- which naturally incorporates this measure. We investigate how network structure impacts information flow and find that the quoter model exhibits interesting features similar to those of complex contagion. Finally, we offer an analytical treatment of the quoter model: we derive approximate calculations and show dependence on model parameters. This thesis gives rise to new hypotheses about the role of the social network in facilitating information flow, which future research can investigate using real-world data.
590 $a School code: 0243.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Information science. $3 554358
650 4 $a Statistical physics. $3 536281
653 $a Complex systems
653 $a Entropy
653 $a Information diffusion
653 $a Network science
653 $a Probability
653 $a Social dynamics
690 $a 0364
690 $a 0723
690 $a 0217
710 2 $a The University of Vermont and State Agricultural College. $b Mathematics. $3 3170225
773 0 $t Masters Abstracts International $g 81-10.
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791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834367