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Distortions of political bias in crowdsourced misinformation flagging

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Distortions of political bias in crowdsourced misinformation flagging. / Coscia, Michele; Rossi, Luca.

In: Journal of the Royal Society. Interface, Vol. 17, No. 167, 10.06.2020.

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

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@article{17547d2bf67e4edd933a7363d94bcecf,
title = "Distortions of political bias in crowdsourced misinformation flagging",
abstract = "Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced {\textquoteleft}flags{\textquoteright}: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and—supposedly—reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.",
keywords = "echo chambers, flagging, fake news, social networks, content policing, social media",
author = "Michele Coscia and Luca Rossi",
year = "2020",
month = jun,
day = "10",
doi = "https://doi.org/10.1098/rsif.2020.0020",
language = "English",
volume = "17",
journal = "Journal of the Royal Society. Interface",
issn = "1742-5689",
publisher = "Royal Society, The",
number = "167",

}

RIS

TY - JOUR

T1 - Distortions of political bias in crowdsourced misinformation flagging

AU - Coscia, Michele

AU - Rossi, Luca

PY - 2020/6/10

Y1 - 2020/6/10

N2 - Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced ‘flags’: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and—supposedly—reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.

AB - Many people view news on social media, yet the production of news items online has come under fire because of the common spreading of misinformation. Social media platforms police their content in various ways. Primarily they rely on crowdsourced ‘flags’: users signal to the platform that a specific news item might be misleading and, if they raise enough of them, the item will be fact-checked. However, real-world data show that the most flagged news sources are also the most popular and—supposedly—reliable ones. In this paper, we show that this phenomenon can be explained by the unreasonable assumptions that current content policing strategies make about how the online social media environment is shaped. The most realistic assumption is that confirmation bias will prevent a user from flagging a news item if they share the same political bias as the news source producing it. We show, via agent-based simulations, that a model reproducing our current understanding of the social media environment will necessarily result in the most neutral and accurate sources receiving most flags.

KW - echo chambers

KW - flagging

KW - fake news

KW - social networks

KW - content policing

KW - social media

U2 - https://doi.org/10.1098/rsif.2020.0020

DO - https://doi.org/10.1098/rsif.2020.0020

M3 - Journal article

VL - 17

JO - Journal of the Royal Society. Interface

JF - Journal of the Royal Society. Interface

SN - 1742-5689

IS - 167

ER -

ID: 85143311