Dataset Information

Utility and use of accuracy cues in social learning of crowd preferences.

ABSTRACT: Despite limited information and knowledge, we personally form beliefs about certain properties of objects encountered in our daily life-popularity of a newly released movie, for example. Since such beliefs are prone to error, we often revise our initial beliefs according to the beliefs of others to improve accuracy. Optimal revision requires modulating the degree of accepting others' beliefs based on various cues for accuracy-number of opinions, for example-such that the more accurate others' beliefs are, the more we accept them. Although previous studies have shown that such accuracy cues can influence the degree of acceptance during social revision, they primarily investigated problems with 'factually correct' answers, and rarely problems with 'socially correct' answers. Here we examined which accuracy cues are objectively useful (utility of cues), and how those cues are used (use of cues), in the social revision of people's beliefs about problems with 'socially correct' answers. We asked people to estimate the 'shared preferences (SPs)' for sociocultural items, the answers to which are determined by socially aggregated beliefs-how popular an abstract painting will be among a large crowd, for example-and then to revise their initial estimates after being exposed to other people's estimates about the same items. We considered 'confidence', 'agreement among estimates', and 'number of estimates' as accuracy cues. We found that, while all three cues validly signaled the accuracy of SP estimates, only the 'number' cue has a significant utility, but the other cues are much less useful for optimal revision. Nevertheless, people used the cues of 'agreement' and their own 'confidence' to the extent comparable to that of the 'number' cue. Our findings suggest that the utility and use of accuracy cues for problems with 'socially correct' answers differ from those with 'factually correct' answers, as follows: (i) confidence does not have a significant utility and (ii) but people use their own confidence while ignoring others' confidence.

SUBMITTER: Lim J

PROVIDER: S-EPMC7592789 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Utility and use of accuracy cues in social learning of crowd preferences.

Lim Jaeseob J Lee Sang-Hun SH

PloS one 20201028 10

Despite limited information and knowledge, we personally form beliefs about certain properties of objects encountered in our daily life-popularity of a newly released movie, for example. Since such beliefs are prone to error, we often revise our initial beliefs according to the beliefs of others to improve accuracy. Optimal revision requires modulating the degree of accepting others' beliefs based on various cues for accuracy-number of opinions, for example-such that the more accurate others' be ...[more]

PMID: 33112896

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Project description:BackgroundA scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products.MethodsOur specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted.ResultsAfter screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others.ConclusionsOur results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied.Trial registrationOpen Science Framework ( https://osf.io/kv9hu/ ).

Dataset Information

Utility and use of accuracy cues in social learning of crowd preferences.

Publications

Utility and use of accuracy cues in social learning of crowd preferences.

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