Framing density
This protocol is extracted from research article:
The public and legislative impact of hyperconcentrated topic news
Sci Adv, Aug 28, 2019; DOI: 10.1126/sciadv.aat8296

We contribute the notion of framing density, measured by entropic news keywords. We use entropy between pairs of temporally disparate news corpora (as described earlier) to rank individual n-grams for their effectiveness in distinguishing the later corpus from the earlier one. Entropic keywords therefore represent the concentration of a news domain at a given time. We define the annual framing density of a given domain as the number of keywords per article required to attain K% of dataset entropy between the present annual corpus and the preceding one. We examined values of K from 50 to 99% and found that the resulting trend appeared to be fairly consistent across this range, although the specific values varied. Our intention is to capture the bulk of the probability mass while ignoring the long tail. We use a value of K = 50% in Fig. 4. We posit, as in Fig. 4, that framing changes tend to be characterized by low values of framing density.

We scale our values of framing density by a constant factor to enable visibility in figures.

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