Crowdsourcing samples for research on violent extremism: A research note
Abstract
As research on violent extremism continues to progress beyond some of the field’s earlier challenges, new ways of gathering primary source data are becoming increasingly popular. One such data collection methodology implemented widely across parallel fields is crowdsourcing: the process of gathering information, or input, from large numbers of people, either for payment or not, online. In this research note, we present a brief introduction to crowdsourcing, highlight a popular platform for gathering samples online, Prolific, and present four studies conducted by the research team to demonstrate the unique benefits and challenges of crowdsourcing samples online for research on violent extremism.
Study specs
- Authors
- C Clemmow,I van der Vegt,B Rottweiler
- Institution
- University College London
- Discipline
- Computational Social Science
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source 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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