Beyond Verification| Algorithmic, But Polyphonic: On the Authenticity of News Stories Covering Anti-Asian Racism

Abstract

What makes something authentic seems both singular and algorithmic, unique yet formulaic. To study what makes news authentic, we conducted a mixed quantitative-qualitative study of 117 news stories covering anti-Asian racism. We identified features of authenticity that frequently appeared in such news, but they were not reliable indicators of authenticity. Authenticity is polyphonic and difficult to decode; it can appear in multiple ways at once, making it difficult to trace back to a fixed set of features that algorithmically produces it. This divisiveness, we contend, came from what these news stories said about anti-Asian racism. Specifically, we found that authenticity appeared divisive when news stories located Asianness in North America while treating Asia as a cultural other—when news coverage of anti-Asian racism was invested in North American multicultural discourse. What makes a news story authentic depends ultimately on who reads it and how.