Research note: Examining potential bias in large-scale censored data

We examine potential bias in Facebook’s 10-trillion cell URLs dataset, consisting of URLs shared on its platform and their engagement metrics. Despite the unprecedented size of the dataset, it was altered to protect user privacy in two ways: 1) by adding differentially private noise to engagement co...

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Autores principales:	Jennifer Allen, Markus Mobius, David M. Rothschild, Duncan J. Watts
Formato:	article
Lenguaje:	EN
Publicado:	Harvard Kennedy School 2021
Materias:	big data facebook fake news Information technology T58.5-58.64 Communication. Mass media P87-96
Acceso en línea:	https://doaj.org/article/80016e4f3a1e416fb698034437bd6baa
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id	oai:doaj.org-article:80016e4f3a1e416fb698034437bd6baa
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spelling	oai:doaj.org-article:80016e4f3a1e416fb698034437bd6baa2021-11-20T05:25:47ZResearch note: Examining potential bias in large-scale censored data10.37016/mr-2020-742766-1652https://doaj.org/article/80016e4f3a1e416fb698034437bd6baa2021-07-01T00:00:00Zhttps://misinforeview.hks.harvard.edu/article/research-note-examining-potential-bias-in-large-scale-censored-data/https://doaj.org/toc/2766-1652We examine potential bias in Facebook’s 10-trillion cell URLs dataset, consisting of URLs shared on its platform and their engagement metrics. Despite the unprecedented size of the dataset, it was altered to protect user privacy in two ways: 1) by adding differentially private noise to engagement counts, and 2) by censoring the data with a 100-public-share threshold for a URL’s inclusion. To understand how these alterations affect conclusions drawn from the data, we estimate the prevalence of fake news in the massive, censored URLs dataset and compare it to an estimate from a smaller, representative dataset. We show that censoring can substantially alter conclusions that are drawn from the Facebook dataset. Because of this 100-public-share threshold, descriptive statistics from the Facebook URLs dataset overestimate the share of fake news and news overall by as much as 4X. We conclude with more general implications for censoring data.Jennifer AllenMarkus MobiusDavid M. RothschildDuncan J. WattsHarvard Kennedy Schoolarticlebig datafacebookfake newsInformation technologyT58.5-58.64Communication. Mass mediaP87-96ENHarvard Kennedy School Misinformation Review, Vol 2, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	big data facebook fake news Information technology T58.5-58.64 Communication. Mass media P87-96
spellingShingle	big data facebook fake news Information technology T58.5-58.64 Communication. Mass media P87-96 Jennifer Allen Markus Mobius David M. Rothschild Duncan J. Watts Research note: Examining potential bias in large-scale censored data
description	We examine potential bias in Facebook’s 10-trillion cell URLs dataset, consisting of URLs shared on its platform and their engagement metrics. Despite the unprecedented size of the dataset, it was altered to protect user privacy in two ways: 1) by adding differentially private noise to engagement counts, and 2) by censoring the data with a 100-public-share threshold for a URL’s inclusion. To understand how these alterations affect conclusions drawn from the data, we estimate the prevalence of fake news in the massive, censored URLs dataset and compare it to an estimate from a smaller, representative dataset. We show that censoring can substantially alter conclusions that are drawn from the Facebook dataset. Because of this 100-public-share threshold, descriptive statistics from the Facebook URLs dataset overestimate the share of fake news and news overall by as much as 4X. We conclude with more general implications for censoring data.
format	article
author	Jennifer Allen Markus Mobius David M. Rothschild Duncan J. Watts
author_facet	Jennifer Allen Markus Mobius David M. Rothschild Duncan J. Watts
author_sort	Jennifer Allen
title	Research note: Examining potential bias in large-scale censored data
title_short	Research note: Examining potential bias in large-scale censored data
title_full	Research note: Examining potential bias in large-scale censored data
title_fullStr	Research note: Examining potential bias in large-scale censored data
title_full_unstemmed	Research note: Examining potential bias in large-scale censored data
title_sort	research note: examining potential bias in large-scale censored data
publisher	Harvard Kennedy School
publishDate	2021
url	https://doaj.org/article/80016e4f3a1e416fb698034437bd6baa
work_keys_str_mv	AT jenniferallen researchnoteexaminingpotentialbiasinlargescalecensoreddata AT markusmobius researchnoteexaminingpotentialbiasinlargescalecensoreddata AT davidmrothschild researchnoteexaminingpotentialbiasinlargescalecensoreddata AT duncanjwatts researchnoteexaminingpotentialbiasinlargescalecensoreddata
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Research note: Examining potential bias in large-scale censored data

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