This article uses scaled entity search (SES), a data mining technique and interpretive framework, to analyze radio stations’ strategies of self-distinction and promotion within the broadcasting trade press. The authors model the technique by comparing the frequency of mentions of over two thousand call signs within the 1.5 million page digitized corpus of the Media History Digital Library. Using the ranked distribution of about two thousand results, the article compares the strategies of a top-trending station, WCCO Minneapolis, to those of stations at the twenty-fifth, fiftieth, and seventy-fifth percentile of results to achieve a bird’s-eye view of station advertising practices across a much larger field of practice than is possible with traditional close reading. Although the prominence of often dramatic and eye-catching advertisements in journals such as Broadcasting and Sponsor suggest that station advertising was a widespread industrial practice, SES indicates that the practice was primarily taken up by only the largest stations, those with (local and national) network affiliation, and those represented by highly active station intermediaries.
- Big Data