Using impression data to improve models of online social influence

Abstract Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and commen...

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Autores principales:	Rui Liu, Kevin T. Greene, Ruibo Liu, Mihovil Mandic, Benjamin A. Valentino, Soroush Vosoughi, V. S. Subrahmanian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/614a3eed5d5c41fca2d67b914c10f39e
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spelling	oai:doaj.org-article:614a3eed5d5c41fca2d67b914c10f39e2021-12-02T16:45:46ZUsing impression data to improve models of online social influence10.1038/s41598-021-96021-32045-2322https://doaj.org/article/614a3eed5d5c41fca2d67b914c10f39e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96021-3https://doaj.org/toc/2045-2322Abstract Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and comments), but neglect impressions (exposure to content, such as views) and lack “ground truth” measures of influence. To overcome these limitations, we implemented a social media simulation using an original, web-based micro-blogging platform. We propose three influence models, leveraging expressions and impressions to create a more complete picture of social influence. We demonstrate that impressions are much more important drivers of influence than expressions, and our models accurately identify the most influential accounts in our simulation. Impressions data also allow us to better understand important social media dynamics, including the emergence of small numbers of influential accounts and the formation of opinion echo chambers.Rui LiuKevin T. GreeneRuibo LiuMihovil MandicBenjamin A. ValentinoSoroush VosoughiV. S. SubrahmanianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rui Liu Kevin T. Greene Ruibo Liu Mihovil Mandic Benjamin A. Valentino Soroush Vosoughi V. S. Subrahmanian Using impression data to improve models of online social influence
description	Abstract Influence, the ability to change the beliefs and behaviors of others, is the main currency on social media. Extant studies of influence on social media, however, are limited by publicly available data that record expressions (active engagement of users with content, such as likes and comments), but neglect impressions (exposure to content, such as views) and lack “ground truth” measures of influence. To overcome these limitations, we implemented a social media simulation using an original, web-based micro-blogging platform. We propose three influence models, leveraging expressions and impressions to create a more complete picture of social influence. We demonstrate that impressions are much more important drivers of influence than expressions, and our models accurately identify the most influential accounts in our simulation. Impressions data also allow us to better understand important social media dynamics, including the emergence of small numbers of influential accounts and the formation of opinion echo chambers.
format	article
author	Rui Liu Kevin T. Greene Ruibo Liu Mihovil Mandic Benjamin A. Valentino Soroush Vosoughi V. S. Subrahmanian
author_facet	Rui Liu Kevin T. Greene Ruibo Liu Mihovil Mandic Benjamin A. Valentino Soroush Vosoughi V. S. Subrahmanian
author_sort	Rui Liu
title	Using impression data to improve models of online social influence
title_short	Using impression data to improve models of online social influence
title_full	Using impression data to improve models of online social influence
title_fullStr	Using impression data to improve models of online social influence
title_full_unstemmed	Using impression data to improve models of online social influence
title_sort	using impression data to improve models of online social influence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/614a3eed5d5c41fca2d67b914c10f39e
work_keys_str_mv	AT ruiliu usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT kevintgreene usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT ruiboliu usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT mihovilmandic usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT benjaminavalentino usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT soroushvosoughi usingimpressiondatatoimprovemodelsofonlinesocialinfluence AT vssubrahmanian usingimpressiondatatoimprovemodelsofonlinesocialinfluence
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Using impression data to improve models of online social influence

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