Why do people share political information and misinformation online? Developing a bottom-up descriptive framework

22 citations

Abstract

Social media users are key actors in the spreading of misleading or incorrect information. To develop an integrative parsimonious summary of social media users' own accounts of motives for sharing political information, we conducted: (1) a literature review of motives for personally sharing false information as reported by social media users and (2) qualitative research concerning these motives using an innovative, ecologically valid method. Based on our findings, we developed a pool of items evaluating social media users' motives for sharing false political information, which we then tested and analyzed the dimensionality of in (3) a pre-registered questionnaire-based study to identify key clusters of users' own accounts of motives for sharing both true and false political information. The current findings show that there are distinct sets of motives people report for their misinformation sharing behavior: prosocial activism, attack or manipulation of others, entertainment, awareness, political self-expression, and fighting false information. Also, these sets of motives are associated with variables known to predict sharing misinformation, and some of these sets predict social media users' self-reports of having shared misinformation in the past. Our findings highlight and elaborate on users' motives that reflect a concern with "making things better" and acting in a manner that is beneficial to society as a whole, and suggest that different interventions may be required to combat misinformation sharing driven by different motives. A potential set of 18 items that could be used in questionnaires measuring motivations for sharing political news online is described.

22
Citations
Research
Paper Only

Study specs

Discipline
Social Science
Year
2023
Human Data Platform
Prolific

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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