Evidence map›Paper›PMID 37520838›Full record

ArticleInternet of things (Amsterdam, Netherlands)2022

"Hey Alexa, what do you know about the COVID-19 vaccine?"- (Mis)perceptions of mass immunization and voice assistants.

Filipo Sharevski, Anna Slowinski, Peter Jachim, Emma Pieroni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Internet of things (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Filipo SharevskiCollege of Computing and Digital Media, DePaul University, 243 S Wabash Avenue, 60604, Chicago, IL, United States of America.
Anna SlowinskiCollege of Computing and Digital Media, DePaul University, 243 S Wabash Avenue, 60604, Chicago, IL, United States of America.
Peter JachimCollege of Computing and Digital Media, DePaul University, 243 S Wabash Avenue, 60604, Chicago, IL, United States of America.
Emma PieroniCollege of Computing and Digital Media, DePaul University, 243 S Wabash Avenue, 60604, Chicago, IL, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we analyzed the perceived accuracy of COVID-19 vaccine information spoken back by Amazon Alexa. Unlike social media, Amazon Alexa does not apply soft moderation to unverified content, allowing for use of third-party malicious skills to arbitrarily phrase COVID-19 vaccine information. The results from a 210-participant study suggest that a third-party malicious skill could successful reduce the perceived accuracy among the users of information as to who gets the vaccine first, vaccine testing, and the side effects of the vaccine. We also found that the vaccine-hesitant participants are drawn to pessimistically rephrased Alexa responses focused on the downsides of the mass immunization. We discuss solutions for soft moderation against misperception-inducing or other malicious third-party skills.

Indexed as

COVID-19 misinformationIoT securityMalicious skillsSecurity and privacy: Usability in security and privacySocial engineering attacksVoice assistants security

Identifiers

PMID37520838
PMCPMC9264809

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.