Multiple searches increase the impact of similarly biased search results: An example of the “multiple exposure effect” (MEE)

69 Pages Posted: 30 Nov 2023

See all articles by Robert Epstein

Robert Epstein

American Institute for Behavioral Research and Technology (AIBRT)

Miki Ding

American Institute for Behavioral Research and Technology (AIBRT)

Christine Mourani

American Institute for Behavioral Research and Technology (AIBRT)

Amanda Newland

American Institute for Behavioral Research and Technology (AIBRT)

Emily Olson

American Institute for Behavioral Research and Technology (AIBRT)

Felix Tran

American Institute for Behavioral Research and Technology (AIBRT)

Date Written: November 17, 2023

Abstract

A series of experiments published in the Proceedings of the National Academy of Sciences in 2015 showed that search engine results favoring one candidate can (a) shift the preferences of undecided voters by up to 80% in some demographic groups and (b) be masked so people show no awareness of the manipulation. We labeled this phenomenon the Search Engine Manipulation Effect (SEME), and it appears to be one of the largest behavioral effects ever discovered. In two follow-up experiments with a total of 1,032 undecided, eligible U.S. voters (mean age = 32.6), we have now replicated SEME with a different election (the 2016 Clinton/Trump election) and different conditions. All previous SEME experiments allowed subjects just one search opportunity. In the new experiments, we asked whether multiple searches on the same topic (but using somewhat different search terms) would shift voting preferences more than a single search would. In both of the new experiments all subjects were first shown brief biographies of the candidates, then asked about their voting preferences, then allowed to conduct an online search using our mock search engine (Kadoodle), and then asked again about their voting preferences. In both experiments, subjects in different groups saw search rankings favoring Mr. Trump, Mrs. Clinton, or neither. In Experiment 1, all search sessions lasted a maximum of 15 minutes, as in previous experiments. In Experiment 2, all search sessions were limited to 5 minutes, which is more typical of real search behavior. Our primary dependent variable was Vote Manipulation Power (VMP), the percentage increase in the number of subjects inclined to vote for one candidate after having viewed search rankings favoring that candidate. In Experiment 1, a VMP of 11.5% was found for groups conducting just one search. In the multiple-search conditions, the VMP increased with successive searches from 14.3% to 20.2% to 22.6%. In Experiment 2, a VMP of 8.0% was found for groups conducting just one search. In the multiple search conditions, the VMP increased with successive searches from 9.6% to 19.3% to 25.3%. Corresponding shifts were also found for how much subjects reporting liking and trusting the candidates and for subjects’ overall impression of the candidates. Because multiple, short searches are typical of user behavior, we conclude that our previous reports about the possible impact that similarly biased search results might have underestimated the impact that biased search rankings can have on people conducting multiple searches over time. Findings in the multiple-search experiments exemply what we call the “multiple exposure effect” (MEE); we summarize data from other studies which also appear to demonstrate the cumulative effects of multiple exposures to similarly biased online content.

Keywords: search engines, Search Engine Manipulation Effect, SEME, online manipulation, multiple search, Vote Manipulation Power, MEE, Multiple Exposure Effect

Suggested Citation

Epstein, Robert and Ding, Miki and Mourani, Christine and Newland, Amanda and Olson, Emily and Tran, Felix, Multiple searches increase the impact of similarly biased search results: An example of the “multiple exposure effect” (MEE) (November 17, 2023). Available at SSRN: https://ssrn.com/abstract=4636728 or http://dx.doi.org/10.2139/ssrn.4636728

Robert Epstein (Contact Author)

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

Miki Ding

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

Christine Mourani

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

Amanda Newland

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

Emily Olson

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

Felix Tran

American Institute for Behavioral Research and Technology (AIBRT) ( email )

United States

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