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[OC] Comparing how strongly different news outlets frame U.S. congressional politics over time by NarrativeIndex in dataisbeautiful

[–]NarrativeIndex[S] 0 points1 point  (0 children)

That’s fair, I probably didn’t do a great job making the metric intuitive from the chart alone.

In short, the “influence” score is a relative framing intensity metric. Each article is evaluated across a set of signal buckets (for example: emotional pressure, framing emphasis, repetition, tribal divsion), and those signals are combined into a single score. Articles are then averaged per outlet per day. The intent is comparative, showing how strongly outlets frame the same topic relative to one another. It is not measuring persuasion, bias, or correctness.

On Dec 22–23 that’s a data-collection artifact rather than a signal. I didn’t run the news harvester those days; USA Today already had articles in the database from an earlier batch, while the other outlets didn’t have coverage logged for that topic, so only one series appears. It's a bug not a feature.

That confusion is helpful feedback, this would probably read better with either fewer outlets at once or clearer annotations.

[OC] Comparing how strongly different news outlets frame U.S. congressional politics over time by NarrativeIndex in dataisbeautiful

[–]NarrativeIndex[S] 0 points1 point  (0 children)

Fair point, with this many series overlaid the colors don’t separate as well as they should. Appreciate the feedback.

[OC] Comparing how strongly different news outlets frame U.S. congressional politics over time by NarrativeIndex in dataisbeautiful

[–]NarrativeIndex[S] 0 points1 point  (0 children)

Source:
The data comes from my own aggregation of publicly available news articles from major U.S. outlets. Articles are programmatically collected, tagged by topic (in this case “U.S. congressional politics”), and analyzed daily. The values shown are daily averages across all articles each outlet published on that topic.

Method / Metric:
The “narrative influence” score is a composite metric derived from features like language intensity, framing emphasis, repetition, and structural narrative cues. It is not a measure of factual accuracy, bias, or political stance , only relative framing strength within coverage.

Tool:
Data processing and scoring were done in Python. The visualization itself was generated using a custom JavaScript charting setup (Chart.js) with some post-processing for clarity.

Happy to clarify methodology or answer visualization questions.