What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME)

Robert Epstein, Christina Tyagi and Hongyu Wang

PLOS ONE, 2023, vol. 18, issue 7, 1-25

Abstract: The internet has made possible a number of powerful new forms of influence, some of which are invisible to users and leave no paper trails, which makes them especially problematic. Some of these effects are also controlled almost exclusively by a small number of multinational tech monopolies, which means that, for all practical purposes, these effects cannot be counteracted. In this paper, we introduce and quantify an effect we call the Targeted Messaging Effect (TME)–the differential impact of sending a consequential message, such as a link to a damning news story about a political candidate, to members of just one demographic group, such as a group of undecided voters. A targeted message of this sort might be difficult to detect, and, if it had a significant impact on recipients, it could undermine the integrity of the free-and-fair election. We quantify TME in a series of four randomized, controlled, counterbalanced, double-blind experiments with a total of 2,133 eligible US voters. Participants were first given basic information about two candidates who ran for prime minister of Australia in 2019 (this, to assure that our participants were “undecided”). Then they were instructed to search a set of informational tweets on a Twitter simulator to determine which candidate was stronger on a given issue; on balance, these tweets favored neither candidate. In some conditions, however, tweets were occasionally interrupted by targeted messages (TMs)–news alerts from Twitter itself–with some alerts saying that one of the candidates had just been charged with a crime or had been nominated for a prestigious award. In TM groups, opinions shifted significantly toward the candidate favored by the TMs, and voting preferences shifted by as much as 87%, with only 2.1% of participants in the TM groups aware that they had been viewing biased content.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0284495

DOI: 10.1371/journal.pone.0284495

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