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      Manipulative tactics are the norm in political emails: Evidence from 300K emails from the 2020 US election cycle

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          Abstract

          We collect and analyze a corpus of more than 300,000 political emails sent during the 2020 US election cycle. These emails were sent by over 3000 political campaigns and organizations including federal and state level candidates as well as Political Action Committees. We find that in this corpus, manipulative tactics—techniques using some level of deception or clickbait—are the norm, not the exception. We measure six specific tactics senders use to nudge recipients to open emails. Three of these tactics—“dark patterns”—actively deceive recipients through the email user interface, for example, by formatting “from:” fields so that they create the false impression the message is a continuation of an ongoing conversation. The median active sender uses such tactics 5% of the time. The other three tactics, like sensationalistic clickbait—used by the median active sender 37% of the time—are not directly deceptive, but instead, exploit recipients’ curiosity gap and impose pressure to open emails. This can further expose recipients to deception in the email body, such as misleading claims of matching donations. Furthermore, by collecting emails from different locations in the US, we show that senders refine these tactics through A/B testing. Finally, we document disclosures of email addresses between senders in violation of privacy policies and recipients’ expectations. Cumulatively, these tactics undermine voters’ autonomy and welfare, exacting a particularly acute cost for those with low digital literacy. We offer the complete corpus of emails at https://electionemails2020.org for journalists and academics, which we hope will support future work.

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          hdbscan: Hierarchical density based clustering

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            Less than you think: Prevalence and predictors of fake news dissemination on Facebook

            Fake news sharing in 2016 was rare but significantly more common among older Americans.
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                Author and article information

                Contributors
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                Journal
                Big Data & Society
                Big Data & Society
                SAGE Publications
                2053-9517
                2053-9517
                January 2023
                January 23 2023
                January 2023
                : 10
                : 1
                : 205395172211453
                Affiliations
                [1 ]Department of Computer Science, Princeton University, Princeton, USA
                [2 ]Department of Sociology, Ludwig Maximilian University of Munich, Munich, Germany
                [3 ]Department of Sociology and Office of Population Research, Princeton University, Princeton, USA
                Article
                10.1177/20539517221145371
                e84f477d-0fdf-45c8-b462-fa4a2456448e
                © 2023

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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