SEO as Audience Analysis: Accounting for Algorithms in Content Strategy.

Purpose: This project contributes a rhetorical approach to search engine optimization (SEO) as algorithmic audience analysis. It positions SEO as an activity that requires strategists to compose website content that is optimized to both human search engine users and the algorithmic audience (Gallagher, 2017) of a search engine's indexed content. Method: Actor-Network Theory (Latour, 2005), with its focus on the agency of non-human entities combined with human agency in social activity, provides the theoretical framework for this approach. The project combines usability testing with web development methods to trace rhetorical agency during online search activities (Hocutt, 2019). Doing so demonstrates the role search algorithms play as receptive audiences of SEO strategies. Results: Approaches to teaching SEO within the framework of technical and professional communication (TPC) rhetorical foundations require understanding the algorithmic audiences of SEO practices. By matching timestamp data from videorecorded usability tests and HTTP archive (HAR) files produced during usability testing sessions, content strategists can overlay the chronological recordings with their SEO strategies to better understand how successfully SEO met human and algorithmic audience expectations. When SEO practice identifies human audience expectations effectively and develops content signals attractive to its technological audiences, both audiences succeed in an assembled meaning-making exercise. By applying existing methods of audience analysis to search algorithms, content strategists can improve SEO and help surface relevant content for their human users. Conclusion: The results of this project provide a framework for practicing SEO as rhetorical activity built upon audience analysis of both human and non-human users. [ABSTRACT FROM AUTHOR]