This dataset combines 11 monthly surveys with 15,000 total participants to investigate news discernment patterns in the U.S. It measures how informed voters are about political news, finding that 47% of subjects confidently choose a true story over a fake one, while 3% choose the fake. The analysis links discernment to socioeconomic differences and partisan congruence.
Use Cases
- Analyze the association between socioeconomic differences and the probability of selecting a true news story.
- Model the impact of partisan congruence between an individual and a news story on news discernment.
- Investigate patterns in subject confidence levels (confident, uncertain) when confronted with true and fake news stories.
Strengths
- Data is based on 11 monthly surveys, providing a repeated cross-sectional view.
- Includes responses from 15,000 total participants, a substantial sample size.
- Quantifies key outcomes: 47% confidently choose true stories, 3% choose fake stories, and half are uncertain.
Limitations
- The specific column structure and raw data format are unknown, limiting immediate analytical use.
- The dataset's temporal coverage and exact geographic granularity within the U.S. are not specified.
- The methodology for identifying 'major political news stories' is not detailed, which may affect reproducibility.
Provenance
- Source
- ICPSR Harvested Dataverse
- Collection Method
- Combines a protocol for identifying major political news stories with 11 monthly surveys.
- Time Range
- null
- Freshness
- null
- Geography
- United States (U.S. citizens aged 18 and over)