Algorithmic outrage

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


What is the relationship between public outrage and network algorithms which allow social media to operate? The Facebook feed. Google News. Twitter. Each of these platforms draws attention for specific ways algorithms aggregate information, persuade, spread rumors, attract attention, and get audiences angry. Digital outrage, in particular, deserves attention regarding algorithmic influence. Algorithms can capture keywords or posts, but such algorithms can only capture immediacy; i.e., the layered, aggregated nature of digital outrage code cannot detect. This essay proposes that an algorithm of outrage is not a software or code issue (as an algorithm is typically understood) but one of network and ideological algorithms. Digital outrage depends on more than one network to exist. Visualizing these networks via data analysis cannot track or trace actors in a given network - such as those on Facebook, in academia, or in a college or university - because networks layer. Outrage at a representation, public policy, war, Facebook scandal, or a current president always exists within a network of interactions (belief, encounter, texts, responses) but that network must exist within a series of layered networks as well, each with contrasting and complimentary interactions. Networks, layered among each other, make the visible invisible (as opposed to Latour's concern with making the invisible visible). To further this point, this essay explores two particular cases: a widely circulated photograph of Kellyanne Conway kneeling in the Oval Office on a sofa during a visit by HBCU presidents and another widely circulated photograph of former San Francisco 49ers quarterback Collin Kaepernick kneeling during the national anthem. Both events sparked (and continue to spark) digital outrage. But why? What network and ideological algorithms create this outrage? To understand how algorithmic outrage functions and spreads, the layers must be slowly unraveled.

Original languageEnglish
Article number102582
JournalComputers and Composition
StatePublished - Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Inc.

ASJC Scopus subject areas

  • General Computer Science
  • Language and Linguistics
  • Education
  • Linguistics and Language


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