DS-16 · What the coverage emphasizes

Take one well-defined local or regional news topic over a fixed window. Do outlets differ measurably in what they emphasize — which entities and themes co-occur — and can you show it without claiming to have measured bias?

GDELT Global Knowledge Graph (https://www.gdeltproject.org/data.html).

GDELT's tone and theme codes are automated and noisy; treating them as ground truth is the central error. Outlet volume varies by orders of magnitude, so raw counts measure output, not emphasis. "Bias" is not measurable with this data, and students reach for the word immediately.

A comparative emphasis analysis — normalized entity and theme co-occurrence by outlet — with automated-coding limitations stated, and a deliberate, written refusal to characterize outlets as biased.

CT: distinguishing emphasis from bias rigorously. CR: designing a volume-normalized comparison. CO: peer replication on a different topic window. CM: a finding that survives a hostile reader. CZ: media criticism is high-stakes; precision is the ethic.

A journalist, media researcher, computational social scientist, or librarian reviews the framing at Checkpoint 1 and the language of the conclusions at Checkpoint 2. School-supervised.

R1: scoped to emphasis, one topic, one window. R2: GDELT coding limits stated. R3: raw-count comparison as the naive baseline. R4: normalization effect shown. R5: language is precise throughout. R6: the refusal to claim bias is explicit and reasoned.

(a) The student averages GDELT tone by outlet and publishes a bias ranking built on automated sentiment. (b) Raw article counts are compared and the finding is that large outlets publish more.