Social media regulation, third-person effect, and public views: A comparative study of the United States, the United Kingdom, South Korea, and Mexico
Abstract
Given the prevalence of misinformation on social media and accompanying negative externalities, platform regulation has become a highly contested public issue globally. This study investigated (a) what global publics think about platform regulation and (b) the psychological mechanisms underlying such opinions through the lens of the third-person effect. Four national surveys, conducted in the United States, the United Kingdom, South Korea, and Mexico in April--September 2021, revealed that both presumed media influence on self and others play important but different roles in predicting support for two distinctive forms of platform regulation (i.e. government regulation *of* social media platforms versus content moderation *by* social media platforms). Self-efficacy (self-perceived ability to spot misinformation) and other-efficacy (perception of others' ability to spot misinformation) were identified as two crucial antecedents of third-person perception. There were also nuanced but noteworthy differences in public attitudes toward platform regulations across the four countries studied.
Study specs
- Discipline
- Social Science,Humanities
- Year
- 2022
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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