Human vs. AI Content Labeling Study Data on Perceived Effort and Curation

Supplementary file 1_Human-made vs. AI-generated: how provenance labels drive strategic curation via perceived effort.docx is a 23.5 MB document containing data from a between-subjects experiment with 618 short-form video users. The study, authored by Han Sol Lim and last updated in June 2026, examined how AI-generated and human-made labels affect perceived creator effort and strategic curation intentions. The data is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Analyzing the effect of AI-generated vs. human-made labels on perceived creator effort based on the experimental design described.
• Modeling the dual-pathway mechanism linking perceived effort to strategic curation intentions as outlined in the results.
• Investigating the relationship between algorithmic knowledge and user intervention intention based on the exploratory finding mentioned.

Strengths

• Data originates from a controlled experiment with 618 participants, providing a basis for statistical analysis.
• The study design uses a 3x2 factorial structure (label type × content type) to test specific hypotheses.
• Documentation includes detailed methodological and results sections describing the psychological mechanisms tested.

Limitations

• The dataset is a single 23.5 MB DOCX file; the underlying data structure and variables are not explicitly defined.
• Row count and column-level documentation are unknown, requiring manual inspection after download.
• The description does not specify the geographic or demographic scope of the 618 participants, which may limit generalizability.

Provenance

Source

Han Sol Lim via figshare

Collection Method

Data collected via a between-subjects online experiment.

Freshness

Last updated 2026-06-03 06:04:51