Metadata Analysis Sequence: Unboxing Circulation on Social Media for Multimodal Composition

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D)

Department

Literature and Languages

Date of Award

Spring 2025

Abstract

On social media platforms, knowing how circulation works is a critical part of multimodal writing. Teaching students how to write for social media means teaching them how to produce texts that will reach their intended audiences. However, Multimodal Composition approaches to producing texts for social media are often too focused on algorithms. A widely accepted concession, that algorithms are unknowable, engenders algorithm-centric approaches to multimodality that seek to unbox algorithms to determine their mathematical formulas and logics. These approaches assume that knowing algorithms is the key to producing content that will circulate. But algorithm-centric approaches sometimes overlook the many other factors that contribute to circulation. This project proposes a new methodology, the Metadata Analysis Sequence (MAS), that has researchers and students identify what happens on social medial platforms to move content. Rather than determining how algorithms rank content, they explore how user interactions and platform conditions along with algorithms work together to make circulation happen. This project uses the MAS to analyze YouTube’s metadata types to gain insight on how it circulates content and what writing students can do to increase the chances of their content circulating on the platform. This project’s findings are also made relatable to rhetorical theories and writing practices already used in multimodal composition classrooms. For Multimodal Composition scholars, knowing how to examine social media platforms to determine how circulation works opens the door to more informed and effective classroom instruction.

Advisor

Charles Woods

Subject Categories

Arts and Humanities | Rhetoric and Composition

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