A Burkean Poetic Frames Analysis of the 2004 Presidential Ads

Abstract

While numerous studies have examined the frequency of attack ads in presidential elections, this study measures the level of severity of the attacks. Using Burke's poetic frames as a foundation, this content analytical study examines the 2004 presidential ads. The negativity level of the ads is explored with comparisons made between those by the candidates and the organizations, differences in ads by medium (television, radio, and Internet), and differences in three time periods of the election (pre-conventions, between conventions, and post-conventions). Finally, implications are addressed concerning Burkean frames, 527 groups, and the Internet.

Keywords:

• Attack Ads
• Burke
• Internet
• Poetic Frames
• Presidential Ads

Notes

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Note. Ads with no attacks were listed as “None.” Percentages measure what percentage of the attacks was of this frame, so the category of “None” was not included.

Additional information

Notes on contributors

Brian T. Kaylor

Brian T. Kaylor (MA, University of Missouri, 2005) is a doctoral candidate at the University of Missouri.