Jack Bandy

This book explores the role of algorithmic media services (e.g. Instagram, Google, Twitter/X, TikTok) in mass communication. It provides empirical analyses of these media as digital environments which shape news distribution, advertising practices, and interpersonal communication. Drawing on interdisciplinary research from media studies, computer science, and communication, the book separates evidence-based insights from widespread “folk theories” about big tech companies and their role in society. By focusing on structural changes introduced by algorithmic mediation, this work contributes to ongoing discussions about digital literacy, regulation of big tech, and the future of democratic discourse. The structural focus also provides theoretical insights regarding dispersed gatekeeping roles in algorithmic media environments, the communication dynamics surrounding enshittification, and emerging forms of digital capital in the age of “influencers.” Overall, the book offers a pragmatic perspective toward the rapidly-evolving digital communication environment, emphasizing human agency and the potential for more purposeful engagement with algorithmic media.

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.