The search suggestion effect (SSE): A quantification of how autocomplete search suggestions could be used to impact opinions and votes

https://doi.org/10.1016/j.chb.2024.108342Get rights and content
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Abstract

News reports in 2016 suggested that a leading search engine was suppressing negative search suggestions for one US Presidential candidate but not for her opponent. We conducted a progressive series of five experiments to determine what effect differential suppression of this type might have on voters. We found that negative suggestions attract far more clicks than neutral or positive ones, consistent with extensive research on negativity bias, and that the differential suppression of negative search suggestions can turn a 50/50 split among undecided voters into more than a 90/10 split favoring the candidate for whom negative search suggestions were suppressed. We conclude that differentially suppressing negative search suggestions can have a dramatic impact on the opinions and voting preferences of undecided voters, potentially shifting a large number of votes without people knowing and without leaving a paper trail for authorities to trace.

Keywords

Search suggestion effect
SSE1
Autocomplete
Online influence
Search engine manipulation effect
Negativity bias

Data availability

Data will be made available on request.

Cited by (0)

Robert Epstein (PhD, Harvard University) is Senior Research Psychologist at the American Institute for Behavioral Research and Technology.
Savannah Aries (BA, University of California, Los Angeles) is a research intern at the American Institute for Behavioral Research and Technology.
Kelly Grebbien (BA, California Baptist University) is a graduate student at Liberty University.
Alyssa M. Salcedo (BA, California Baptist University) is a graduate student at the University of California Los Angeles.
Vanessa R. Zankich (BS, University of California, San Diego) is Research Director at the American Institute for Behavioral Research and Technology.