Partisan Media Discourse on Fake News in the U.S., 2020–2024

Mamunor Rashid's study analyzes the rhetorical use of the term "fake news" in U.S. media from 2020 to 2024. The dataset comprises a quantitative content analysis of 1,972 news items from nine left-leaning, centrist, and right-leaning outlets. It was last updated on figshare in May 2026.

Use Cases

• Train NLP models to classify partisan rhetoric based on usage patterns of the term "fake news".
• Analyze agenda-setting and framing strategies across the political spectrum based on the described content analysis.
• Study the evolution of media discourse and credibility challenges over the 2020–2024 period.

Strengths

• Analysis of 1,972 news items provides a substantive sample for study.
• Explicitly compares left-leaning, centrist, and right-leaning media outlets.
• Covers a defined four-year period from 2020 to 2024.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying coded data is unknown, which may limit suitability assessment.
• The 58.9 MB size suggests the primary file is a PDF, which may require text extraction for computational analysis.

Provenance

Source

figshare, author Mamunor Rashid.

Collection Method

Quantitative content analysis of news items from nine media outlets.

Time Range

2020–2024

Freshness

Last updated 2026-05-21 13:19:57; freshness should be verified.

Geography

United States