International Journal of Communication 20(2026) Algorithmic, But Polyphonic
Algorithmic, But Polyphonic: On the Authenticity of News Stories Covering Anti-Asian Racism
PREM XAVIER SYLVESTER[1]
SAGE HUGHES
MATT CANUTE
Simon Fraser University, Canada
JAVIER RUIZ-SOLER
Independent Scholar, Canada
WENDY HUI KYONG CHUN
Simon Fraser University, Canada
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.
Keywords: authenticity, news stories, anti-Asian racism
Prem Xavier Sylvester: [email protected]
Sage Hughes: [email protected]
Matt Canute: [email protected]
Javier Ruiz-Soler: [email protected]
Wendy Hui Kyong Chun: [email protected]
Date submitted: 2024-12-04
In September 2024, then U.S. vice-presidential candidate J. D. Vance argued it was defensible to make factually untrue claims about immigrants eating pets to get at the truth, saying, “If I have to create stories so that the American media actually pays attention to the suffering of the American people, then that’s what I’m going to do” (Maher & Boyette, 2024, para. 3; emphasis added). In a post-truth era, it would seem the facts do not matter; what matters is the story one tells to make something ring true, even if these inaccurate narratives lead to bomb threats and hatred (Helmore, 2024). What matters is how one crafts an authentic story.
What makes a news story authentic? Answering this question is not easy, especially since it is difficult to define authenticity: you allegedly “know it when you see it” (Zogby, 2016, para. 1). Something authentic should be raw, untampered, unique, even if divisive or controversial (Cmiel & Peters, 2020; Trilling, 1972). At the same time, media studies researchers have argued that authenticity is fundamentally algorithmic, that it follows a set of procedures or a formula (Burton et al., 2023). Consider the centrality of format TV to Donald Trump’s apparent authenticity or the numerous primers and how-to guides that offer step-by-step programs for authentic engagement and leadership (Chun, 2021). Journalists have long followed the inverted pyramid model of storytelling, starting with the most important information and then leading into lesser details (Pöttker, 2003), but even fake news can seem authentic if it follows the right steps. How, then, does one study something that is both singular and algorithmic, unique yet formulaic, and that profoundly shapes how we come to know the world through the news?
To help answer these questions, this article outlines a novel quantitative-qualitative method for studying authenticity at scale. Drawing from the critical media studies literature on authenticity, we first produce a coding schema to analyze 117 news stories covering anti-Asian racism during the COVID-19 pandemic. As we elaborate later, news stories reveal the stakes of authenticity in a so-called post-truth era. Through a frequency analysis, we reveal the most significant features of authenticity in these news stories. To assess whether those features could reliably make a news story seem authentic, we calculate their intercoder agreement or reliability scores. Since authenticity is also inherently divisive, as it can produce different interpretations, we also go beyond standard uses of intercoder reliability that use it solely to select textual units with a high degree of agreement among coders. Instead, we also examine parts of news stories that divide coders. We further deploy quantitative measures of similarity, which tell us which features of authenticity can seem like each other and therefore undermine coder agreement. To study why the authenticity of news stories about anti-Asian racism in particular might be divisive, we turn to critical discourse analysis. Through this study, we reveal how authenticity can be formulaic—algorithmic—while nonetheless generating multiple interpretations. We also reflect on the possibilities and limitations of schematic methods like content analysis to identify what makes a news story authentic.
Authenticity and the News: Literature Review and Theoretical Framework
Research in communication studies has investigated the proliferation of fake news, mis-/disinformation, and other forms of factually inaccurate texts to evaluate how people on social media and elsewhere come to see a news story as authentic—how they authenticate it. Typical definitions of authentication are wedded to how audiences verify the factual accuracy of news (e.g., Aïmeur, Amri, & Brassard, 2023; Tandoc et al., 2018), but scholars across multiple disciplines have noted that such verification—fact checking—although important, has not stemmed the spread of misinformation (Uscinski & Butler, 2013); we need other means to understand the proliferation of such information.
Researchers in critical literature in media and communication studies have responded to this so-called post-truth moment by engaging the rich history and meanings of authenticity, which go beyond factual accuracy (Burton et al., 2023; Enli, 2014). As the root word it shares with “author” and “authority”—autos—indicates, authenticity is linked to the creation of powerful narratives rather than the mere relation of facts. The coincidence of authenticity with fact is a specifically modern development that may no longer sustain, as work in the history of science has shown (Poovey, 1998; Shapin, 1994). What makes an authentic narrative powerful or effective can go beyond the purportedly universal appeal of facts, making authenticity divisive or controversial.
Research from media studies also makes it clear that what matters to authenticity—how something is authenticated—depends on the audiences that encounter it in a mediated form, be it through news stories (Batsell, 2015), on radio and TV (Enli, 2014), or on social media (Tandoc et al., 2018). Unsurprisingly, researchers have argued that audiences accept—even expect—a degree of factual inaccuracy in the authentic (Coupland, 2001). Authenticity is relational; its recognition is ultimately dependent on audiences.
At the same time, literature in critical media studies and business studies treats authenticity as algorithmic (Burton et al., 2023; Goffee & Jones, 2005). Authenticity has been described as having a “checklist” or “recipe,” which media production can follow to establish its outputs as authentic (Enli, 2014). Feminist media studies scholar Sarah Banet-Weiser (2012) has examined numerous primers and how-to guides that offer step-by-step programs for authentic branding and leadership. Scholars have studied how Trump crafted his appeal to deliberately appear populist; his performance of authenticity developed through his reality TV show The Apprentice, which, as format of TV, was both scripted and affectively intense (Chun, 2021; Ouellette, 2016). It is possible, then, to discern what people and media artifacts do to establish their authenticity—to identify the functions that algorithmically generate authenticity.
Reviewing the critical literature on how authenticity is constructed, we identified its key features: self-authentication, emotional intensity, culturally authenticating rhetoric, social validation, transgression of conventions, and branding.
Self-authentication: Scholars in media and communication studies have investigated how an individual asserts themselves as authentic. A person can appear authentically themself when they disclose a personal experience or opinion, allowing them to build intimacy with others and prove they are “real” (Vannini & Franzese, 2008). Those witnessing such expression, which is often (implicitly or explicitly) framed as against or outside cultural or social norms, supposedly receive a “complete view of one’s ‘authentic’ self” (Banet-Weiser, 2012, p. 60). The spontaneous expression of one’s thoughts or beliefs—so that it appears immediate—is also often taken as a sign of authentic communication (Belgrad, 1998).
Emotional intensity: Scholars have explored how emotional expression can convey authenticity. In investigating “dramas of authenticity,” “unprecedented demonstrations of emotionality” or “displays of emotion” were found to structure “real” media events, from extreme games to reality TV (McCarthy, 2009, p. 244). Emotions were found to bring people together, affirming shared feelings, be they positive or negative, and be used to demonstrate how events and experiences really make people feel (Enli, 2014; Serazio, 2023).
Culturally authenticating rhetoric: Authenticity can be constructed by people referencing shared experiences or idioms, such as social media memes (Taylor, 2022) or political identifications (Lewis, 2018), that are rhetorically presented as part of a common culture (Duffy, 2013). Such rhetorical moves work to regulate what counts as part of that culture and what does not—who or what gets to be authentic and who or what does not.
Social validation: Authenticity remains tied to truth and reality, albeit through actors and acts whose ability to assert such facts had been socially established. Scholars in communication and discourse studies have demonstrated how experts and public figures, institutions, and “objective” forms of knowledge like data are discursively established as reliable sources of information on what really happened (Gilpin, Palazzolo, & Brody, 2010; van Leeuwen, 2001). What seems authentic also depends on engaging norms, values, and other social systems that support the apparent presentation of truth (Barta & Andalibi, 2021).
Transgression of conventions: Authenticity can also come from contesting what seems obviously true. Scholars have studied how “authentic” political leaders like Barack Obama and Donald Trump have to speak truths that lie beyond the established facts: they said what they believed rather than what they were expected to say, what was considered socially acceptable or “politically correct” (Enli, 2014; McManus, 2015). In the contemporary media ecosystem, especially with reality TV and social media, people were found to explicitly or implicitly position themselves against—transgressing—a convention or norm to demonstrate what they really think or believe (Chun, 2021). They took positions they construed as unacceptable or unpopular, no matter how much support that opinion may actually have: a “trad wife” against “mainstream feminism,” climate “skepticism” against climate science, or antivaccination against public health measures (Banet-Weiser, 2012; Lewis, 2018).
Branding: Scholars have also investigated how authenticity can be understood beyond the personal. Contemporary brand culture, for one, seeks to create an intimate relationship between the brand and consumers based “on the accumulation of memories, emotions, personal narratives and expectations” (Banet-Weiser, 2012, p. 8) to establish authenticity. Brands—personal or corporate—engage their audiences to seem authentic.
Crucially, the cultural and political dimensions of authenticity transcend its formal construction. Scholars in Asian American and Canadian studies have examined how authenticity is raced and gendered. Asian persons in North America have long been marked as inauthentic citizens, as “forever foreign”; in imaginings of modern American development, subjectivities, and national identity, the Asian position is marked by lengthy histories of exclusion laws, internments, lynchings, and property destruction (Palumbo-Liu, 1999). Asian Americans and -Canadians, scholars argue, have been discursively figured as ambivalently American or Canadian, regardless of their ethnicity, citizenship status, or natality (Lowe, 1996; Miki, 2017).
Taking this further, performance studies scholar Karen Shimakawa (2002) argues that Asian Americanness emerges through
an attempt to circumscribe and radically differentiate something that, although deemed repulsively other is, paradoxically, at some fundamental level, an undifferentiable part of the whole. . . . Asian Americanness thus occupies a role both necessary to and mutually constitutive of national subject formation. (p. 3)
That is, the definition of authentic (white) Americanness depends on the abjection of Asian Americans, a subject position that brings together a plethora of panethnic people (Espiritu, 1992).
Scholars engaged with Asian American studies have demonstrated that these racial—and racist—tropes returned to public discourse during COVID-19 (Cho, 2022; Chun, 2020; Li & Nicholson, 2021), especially in relation to China’s purported role in managing the pandemic (Siu & Chun, 2020). Earlier studies have shown how news media have reproduced racialized figurations of Asianness by invoking tropes of exception, such as the “yellow peril” or “model minority,” when covering Asian life in North America (Ono & Pham, 2009; Yang, 2017). Given past work on the false presumptions that undergirded such discourse (Lyman, 2000), its reappearance is a source of concern for authentic coverage of anti-Asian racism during the pandemic.
We therefore ask what makes North American news coverage of anti-Asian racism seem authentic if its factual accuracy can be contested. Given the algorithmic character of authenticity, we examine the features through which it appears in news stories. In the following section, we explain our methodology for studying the algorithmic generation of authentic news stories.
Methodology
We deploy mixed methods to examine how news stories demonstrate their authenticity. Using an original coding schema, we first conducted a content analysis of North American English language news stories that touched on anti-Asian racism during the COVID-19 pandemic. We used quantitative methods—frequency analysis, intercoder reliability and agreement analysis, and similarity analysis—to understand which features of authenticity were invoked in these news stories. We then deployed critical discourse analysis to assess how authenticity emerges from what news stories say about anti-Asian racism during the COVID-19 pandemic.
Content Analysis
Authenticity Schema (Codebook)
Drawing from our literature review on the features of authenticity—its algorithmic functions—we produced a coding schema, organized into the following categories and (nested) features (see Appendix A for the full schema[2]).
Sampling
We prepared a sample of 117 North American English language news stories from early 2020 to early 2022 that touched on anti-Asian racism during the COVID-19 pandemic. This corpus consisted of three batches. We prepared two batches using manual search queries on Nexis Uni, Google News, and an archival search on various digital media outlets. To determine the best search terms, we reviewed an initial corpus of news articles and identified terms that best related to anti-Asian racism. Subsequently, we sampled news stories based on the following search terms:
Asian immigrant* OR Chinese immigrant* OR anti-Asian* OR Oriental* OR black-Asian* OR Stop AAPI Hate OR attacks on Asians OR against Asian* OR anti-Asian* OR anti-Asian racism OR Black-on-Asian OR Black-on-Asian violence[3]
The third batch comprised news stories circulating on X (formerly known as Twitter) in 2022. We scraped articles linked in posts with the popular, but controversial, hashtag #wuhanflu. For each batch, we removed duplicates and stored the news stories as text files. We then transferred sampling units into the online text annotation platform TagWorks.
Coding Procedures
Six university-level research assistants used the TagWorks platform for content analysis. We first familiarized coders with theories of authenticity, the coding schema, and the experiment design. In training sessions, we conveyed to coders that they should apply the schema according to their own sensibilities.
Research assistants worked in two groups on either “highlight” or “data hunt” tasks. In the highlight stage, three coders would independently read a news story and highlight short sections they found authentic. We then moved coding units with a percentage agreement of 67% or higher to the data hunt group, where three other coders would each assign the categories and features of authenticity from the schema to the excerpted unit. For example, a coder might assign social validation to an excerpt and then code it for features within that category, such as data or logic. They could also divide the excerpt into multiple parts that could each be coded separately. Two codes, however, could not be assigned to the same text in each excerpt. Additionally, coders could view the context of the excerpt on TagWorks.
Quantitative Analysis
Through frequency and agreement analyses, we identified features of authenticity that appeared in news stories most often. We first measured the frequency with which coders observed those features in our corpus. We also used measures of intercoder reliability or agreement to assess how reliably those features could be identified in the text—the degree to which coders agreed on a feature. Agreement among three coders (or more) is often measured via Krippendorff’s alpha, which scores perfect agreement as 1 and absolute disagreement as 0 (Krippendorff, 2004). An alpha of 0.67 or higher is generally taken as an acceptable degree of agreement among coders (p. 242), while features with α < 0.67 are typically discarded from analysis. Since what counts as authentic is prone to disagreement, however, we include features with lower alphas in our analysis. To identify how dis/agreement is distributed in the data, we produced a reliability data matrix (Krippendorf, 2004) that measured the proportion of disagreement, partial agreement, or perfect agreement across coding units.[4]
To determine whether intercoder disagreement could emerge from the similarity between certain features of authenticity, we computed the Jaccard indices among all possible pairs of those features. These indices range from 0 (mutual exclusivity) to 1 (perfect similarity), describing the likelihood of two features being assigned to the same coding unit (i.e., the news excerpt). A higher Jaccard index therefore suggests that, to the coders, those features of authenticity seemed highly similar.
Critical Discourse Analysis
We used critical discourse analysis to examine what news stories said about Asianness to make their coverage of anti-Asian racism seem authentic. We paid particular attention to the parts of these stories in which coders partially agreed on which feature of authenticity appeared in the text. This analysis allowed us to understand how the news stories’ discourses functioned amid social and political relations, processes, and structures (Fairclough, 2013).
Results
Quantitative Content Analysis
Frequency Analysis
In the data hunt task, coders used the schema to assign a feature of authenticity to 3,768 unique textual units extracted from 117 news stories in the highlight task. This task produced 12,708 coded data points: 2,659 from 30 articles in the first batch, 7,962 from 57 articles in the second, and 2,087 from 27 articles in the last batch (Table 1). After removing data not coded by at least three coders, we arrived at the total number of coding units (i.e., data points) for analysis (n = 9,509).
Table 1. Data Counts
|
|
Batch 1 |
Batch 2 |
Batch 3 |
Total |
|
Articles |
31 |
58 |
28 |
117 |
|
Coding units |
1,664 |
5,936 |
1,909 |
9,509 |
We found three categories appeared most often as a sign of authenticity, totaling 82% of coded units (Table 2): culturally authenticating rhetoric (35.7%), within which the most frequently identified features described rhetoric excluding people or ideas (33%) or including them (29%); social validation (28%), within which coders most frequently identified appeals to data or logic (45%) and appeals made to experts or public figures (24%); and transgression of conventions (18.3%), in which attacks on mainstream expert(s), expertise, media, or other institutions (38%) and impatience with them (34%) were most frequent.
The remaining three categories—self-authentication (5.5%), emotional intensity (9.3%), and branding (3.3%)—were less prominent.
Table 2. Frequency Distribution of Categories and Features of Authenticity[5]
|
Code (Category -> Feature) |
Batch 1 (n) |
Batch 2 (n) |
Batch 3 (n) |
Category-wise (%) |
Total (n) |
Total (%) |
|
Self-authentication |
90 |
355 |
76 |
|
521 |
5.5 |
|
Self-emphasis |
60 |
292 |
56 |
78 |
408 |
4.6 |
|
Spontaneity |
27 |
52 |
19 |
19 |
98 |
0.8 |
|
Self, other |
3 |
11 |
1 |
3 |
15 |
0.1 |
|
Emotional intensity |
250 |
548 |
83 |
|
881 |
9.8 |
|
Self-directed negative |
78 |
100 |
10 |
21 |
188 |
2.1 |
|
Self-directed positive |
6 |
36 |
11 |
6 |
53 |
0.6 |
|
Other-directed negative |
120 |
282 |
44 |
51 |
446 |
5.0 |
|
Other-directed positive |
46 |
130 |
18 |
22 |
194 |
2.1 |
|
Transgression |
312 |
1,067 |
363 |
|
1,742 |
15.7 |
|
Impatience |
58 |
421 |
114 |
34 |
593 |
5.1 |
|
Amorality |
66 |
200 |
92 |
21 |
358 |
2.8 |
|
Mainstream attacks |
149 |
369 |
146 |
38 |
664 |
6.7 |
|
Marginal community |
24 |
69 |
10 |
6 |
103 |
0.9 |
|
Trans., other |
15 |
8 |
1 |
1 |
24 |
0.2 |
|
Culturally auth. Rhetoric |
439 |
2369 |
583 |
|
3391 |
35.5 |
|
Irony/humor |
59 |
356 |
112 |
16 |
527 |
5.3 |
|
Figures of speech |
31 |
325 |
98 |
13 |
454 |
4.1 |
|
Rhetorical transgression |
10 |
16 |
19 |
1 |
45 |
0.3 |
|
Including |
142 |
733 |
113 |
29 |
988 |
10.8 |
|
Excluding |
182 |
771 |
175 |
33 |
1,128 |
12.4 |
|
Urgency |
15 |
168 |
66 |
7 |
249 |
2.6 |
|
Social validation |
483 |
1,425 |
755 |
|
2,663 |
30.8 |
|
Expert / public figure |
125 |
289 |
218 |
24 |
632 |
7.5 |
|
Institutions |
60 |
122 |
89 |
10 |
271 |
2.8 |
|
Data/logic |
178 |
678 |
337 |
45 |
1,193 |
15.0 |
|
Rights |
25 |
122 |
28 |
7 |
175 |
1.4 |
|
Values/norms |
95 |
214 |
83 |
15 |
392 |
4.1 |
|
Branding |
90 |
172 |
49 |
|
311 |
2.7 |
|
Brand association |
38 |
72 |
24 |
43 |
134 |
1.2 |
|
Brand distancing |
24 |
59 |
5 |
28 |
88 |
0.9 |
|
Call for participation |
27 |
41 |
20 |
28 |
88 |
0.6 |
|
Branding, other |
1 |
0 |
0 |
0.0 |
1 |
0.0 |
|
Total |
1,664 |
5,936 |
1,909 |
– |
9,509 |
– |
Agreement Analysis
The Krippendorff alphas for the highlight task fluctuated from low to moderate (relative to the nominally reliable alpha of 0.67) across the batches. Despite this disagreement, 3,768 phrases passed the nominal percentage value for intercoder agreement (≥ 67%), allowing them to be coded in the data hunt task.
The three most frequent categories of authenticity—culturally authenticating rhetoric, social validation, and transgression of conventions—had low to moderate Krippendorff alphas, indicating a significant degree of intercoder disagreement (Table 3). However, the most frequent features of social validation—expert / public figure and data/logic—were reliably recognized in the first two batches of news stories (α ≥ 0.67) while dropping significantly in Batch 3. In the third batch of news stories, where articles came from tweets containing “#wuhanflu,” reliability dropped noticeably across all features except for attacks on the mainstream, whose intercoder reliability increased by roughly 6% (α = 0.377).
We also found a high proportion of partial agreement, between 25% and 33% per batch, which the Krippendorff alpha treats as equivalent to disagreement. Though disagreement outnumbered perfect agreement between coders, partial agreement constituted a significant form of dis/agreement (Table 4). Since we were interested in this high proportion of partial agreement, our qualitative analysis focused extensively on it.
Table 3. Krippendorff Alphas of Categories and Features of Authenticity
|
Code (Category -> Feature) |
Batch 1 (α1) |
Batch 2 (α2) |
Batch 3 (α3) |
|
Self-authentication |
0.490 |
0.448 |
0.346 |
|
Self-emphasis |
0.376 |
0.308 |
0.117 |
|
Spontaneity |
0.140 |
0.196 |
-0.047 |
|
Self, other |
-0.011 |
-0.012 |
0.000 |
|
Emotional intensity |
0.558 |
0.575 |
0.340 |
|
Self-directed negative |
0.363 |
0.276 |
0.085 |
|
Self-directed positive |
0.747 |
0.478 |
0.840 |
|
Other-directed negative |
0.256 |
0.402 |
0.488 |
|
Other-directed positive |
0.442 |
0.522 |
0.881 |
|
Transgression |
0.430 |
0.313 |
0.320 |
|
Impatience |
0.058 |
0.100 |
0.064 |
|
Amorality |
0.145 |
0.062 |
0.300 |
|
Attack on mainstream |
0.514 |
0.317 |
0.377 |
|
Marginal community |
-0.029 |
0.466 |
0.490 |
|
Trans., other |
-0.026 |
-0.002 |
0.000 |
|
Culturally authenticating rhetoric |
0.406 |
0.371 |
0.256 |
|
Irony/humor |
0.580 |
0.638 |
0.438 |
|
Figures of speech |
0.365 |
0.523 |
0.463 |
|
Rhetorical transgression |
0.330 |
-0.002 |
-0.009 |
|
Including |
0.404 |
0.468 |
0.267 |
|
Excluding |
0.356 |
0.491 |
0.356 |
|
Urgency |
0.383 |
0.584 |
0.582 |
|
Social validation |
0.587 |
0.520 |
0.350 |
|
Expert / public figure |
0.717 |
0.773 |
0.424 |
|
Institutions |
0.394 |
0.399 |
0.398 |
|
Data/logic |
0.730 |
0.691 |
0.321 |
|
Rights |
0.417 |
0.375 |
-0.008 |
|
Values/norms |
0.548 |
0.548 |
0.279 |
|
Branding |
0.503 |
0.310 |
0.310 |
|
Brand association |
0.092 |
0.380 |
0.134 |
|
Brand distancing |
0.020 |
0.318 |
-0.022 |
|
Call for participation |
-0.075 |
-0.036 |
0.017 |
|
Branding, other |
N/A |
N/A |
N/A |
Table 4. Proportions of Agreement and Disagreement
|
Measure |
Batch 1 (%) |
Batch 2 (%) |
Batch 3 (%) |
|
Disagreement |
44 |
40 |
37 |
|
Partial agreement |
26 |
29 |
31 |
|
Perfect agreement |
30 |
30 |
32 |
Similarity Analysis
Certain pairs of features had an especially high Jaccard index (J)—the coefficient of similarity—across news stories (Table 5). Rhetorics of cultural exclusion in particular were highly similar to the other features of authenticity that coders frequently identified, such as rhetorics of cultural inclusion (J = 0.707), statements from experts / public figures (J = 0.612), the use of data (J = 0.706), or transgressive attacks on mainstream conventions (J = 0.552). Notably, each term of these pairs often had a low Krippendorff alpha, meaning coders found it difficult to distinguish between highly similar features of authenticity.
Table 5. Similarity of the Features of Authenticity
|
Feature 1 |
Feature 2 |
Jaccard Index (J) |
|
Excluding |
Including |
0.707 |
|
Data/logic |
Excluding |
0.706 |
|
Expert/ public figure |
Data/logic |
0.654 |
|
Excluding |
Other-directed negative Emotion |
0.624 |
|
Expert / public figure |
Excluding |
0.612 |
|
Other-directed negative Emotion |
Including |
0.609 |
|
Data/Logic |
Including |
0.606 |
|
Excluding |
Attack on mainstream |
0.552 |
|
Expert / public figure |
Including |
0.535 |
|
Expert / public figure |
Attack on mainstream |
0.517 |
|
Data/logic |
Other-directed negative emotion |
0.510 |
|
Excluding |
Figure of speech |
0.505 |
|
Data/logic |
Attack on mainstream |
0.505 |
|
Irony/humor |
Attack on mainstream |
0.500 |
|
Including |
Figure of speech |
0.500 |
|
Other-directed negative emotion |
Figure of speech |
0.481 |
|
Excluding |
Values/norms |
0.479 |
|
Figure of speech |
Irony/humor |
0.478 |
|
Expert / public figure |
Values/norms |
0.471 |
Discussion of Quantitative Results
Through our quantitative analysis, we identified the features of authenticity that appeared most frequently in news stories and which could algorithmically construct an authentic news story.
The high frequency of culturally authenticating rhetoric (35.7% of the data set) indicated that these stories incorporated cultural touchstones familiar to a social group (e.g., a nationality or community). The boundaries of that context were, on the one hand, defined by who (or what) was included in it (29% of the category). On the other hand, news stories frequently also demonstrated a rhetoric of exclusion (33% of the category), which presented certain actions, people, or ideas as foreign to a cultural space. Such rhetoric enacts a divide—a border—separating certain people or ideas from others, implying who belongs in a group and who does not (Coupland, 2009).
News stories also often cite information from sources whose validity has been established by social systems (28% of the data set). They frequently present facts by citing experts or public figures (24% of the category) and data (45%). These sources of socially authorized knowledge, in evoking the practices of knowledge-production enterprises like scientific inquiry and data analytics (Galison, 2015; Lesage & Hackett, 2013), seem to offer readers the means to access reality. News stories appear authentic, then, by asserting their investment in parsing the facts.
It is not only these references to social and cultural life that support the news’ authenticity; so does calling into question the presumptions that undergird sociality and culture, as indicated by the frequency with which transgressions of conventions appeared in the texts (comprising 18.3% of the data). Transgressive acts frequently took the form of attacks against mainstream convention (38% of the category). News stories appeared “real” by targeting an institution, a form of knowledge (like scientific expertise), or a cultural norm seen as setting an undue limit on some individual or collective activity. Besides outright attacks, expressing impatience with social, political, or moral inertia was also frequently seen as a sign of transgression (34%). Impatience often indexes a collective frustration—or, indeed, exhaustion—with the status quo, even when expressed by individuals (Basta, 2020). News stories often seem authentic when they describe a transgression against the status quo, the crossing of a normative limit.
We also note that features of authenticity that connoted a personal or brand-related stake in collective experiences, knowledge, or norms, comprising modes of self-authentication (5.5%), emotional intensity (9.3%), or branding (3.3%), were rarely coded. An authentic news story, then, does not typically rely on demonstrating a subjective perspective or emotion, or links to commercial interest, which might be seen as signs of partiality (Young, 1990).
The news stories we examined seemed authentic when they rhetorically gestured to a shared cultural context and presented facts authorized by experts and data. These are conventional features of news stories that continue to matter to their authenticity. Differently, challenges to authoritative social actors and cultural precepts matter, too, as seen with news stories describing transgressive acts.
These features, however, were not reliably identified. As we observed through the intercoder agreement scores (measured by Krippendorff alphas), coders disagreed significantly over which features of authenticity they could identify in a news story’s excerpt, even when it did evoke authenticity. Such disagreement is not, however, arbitrary; as we see from the Jaccard indices, coders had difficulty distinguishing features of authenticity that seemed highly similar. The higher the similarity between two features, the more likely that disagreements over recognizing one feature come from its similarity to another. Crucially, news stories demonstrated their authenticity in ways that may have seemed distinct—even oppositional, such as through rhetorics of inclusion and exclusion—but were, in fact, closely related to each other.
We found, then, that the divisiveness inherent to authenticity made it difficult for coders to agree on what made a news story authentic. Even though an authentic news story can be produced by following an algorithm, which functions make up that algorithm—exactly which of its features contribute to a news story’s authenticity—is controversial. News stories can be both formulaic and narratively complex (Schudson, 2005). Even if authenticity is algorithmic—if it is, as per so many how-to guides, formulaic—its recognition is not easily reverse-engineered. Rather, like one-way encryption algorithms, it is difficult to decipher and trace its outcome back to its functions. Authenticity is polyphonic: it can appear in multiple ways at once. One may know authenticity when one sees it, but one may not see it the same way as someone else.
We parsed the polyphony of news authenticity by mapping out coders’ disagreements over which feature of authenticity appeared in a particular part of a news story. For one, authenticating a news story based on the cultural familiarity of its rhetoric can be controversial, as evinced by the low to moderate agreement on where a story demonstrates culturally authenticating rhetoric (α1 = 0.406; α2 = 0.371; α3 = 0.256). Demonstrating cultural familiarity by scripting in a rhetoric of belonging was also controversial: rhetorics of inclusion have low to moderate Krippendorf alphas (α1 = 0.404; α2 = 0.468; α3 = 0.267), as do those of exclusion (α1 = 0.356; α2 = 0.491; α3 = 0.356).
Notably, these two rhetorical forms are often recognized alongside each other—they have a very high similarity score (J = 0.707). What someone might recognize as inclusive rhetoric, another might see as exclusionary. These seemingly contradictory terms are difficult to distinguish from each other: inclusion and exclusion are not two terms of a binary but are interrelated. Such division of a cultural inside from its outside draws on historically constituted norms (Bernard, 2018), but we see in the rhetorical reenactment of that border that it is not easy to separate these cultural spheres (Blomley, 2016). The borders of belonging—its rhetorical contours—were pitched on unsteady ground, making a news story’s use of them to demonstrate its authenticity controversial.
Even when a news story describes the reality of the events and experiences in ways that can be socially validated, its authenticity might be controversial, as we see in disagreements over coding for social validation (α1 = 0.587; α2 = 0.52; α3 = 0.35). Data or statements from public figures and experts are generally taken as reliable signs of authenticity (α1 = 0.73, α2 = 0.691 for the former; α1 = 0.717, α2 = 0.773 for the latter). However, we see this reliability falling to 0.321 in Batch 3 of the news stories we examined, in which, unlike in the previous batches, most stories were seen as evidence for the controversial stance that COVID-19 “leaked” from the Wuhan Institute of Virology. Similarly, the reliability of statements from experts / public figures drops to 0.424 among the controversial claims of Batch 3. When data and experts were called on to validate a socially, culturally, or politically controversial claim, we see that they became less reliable ways of authenticating a news story. Their reliability depends, in other words, on what they speak to, on which claims they support—their value and relevance in a news story are always contextual and contestable (Jasanoff, 2003).
We also found that data and expert statements appeared highly similar to each other (J = 0.654). What matters to the news’ authenticity is how expert statements and data are co-articulated and configured to support a claim. What matters is the constitution of expertise: the “arrangements, devices, concepts, and other actors [that] are necessary if an expert statement or performance is to be formulated, reproduced, . . . disseminated,” and made socially valid (Eyal, 2013, p. 873). What also matters are the ends to which expertise is put. Often, expertise seems to be used to exclude people from a cultural space, as demonstrated by the high similarity of exclusionary rhetoric with expert statements (J = 0.612) and data (J = 0.706).
Similarly, a news story cannot reliably demonstrate its authenticity by scripting in transgressions of conventions—the crossing of normative societal, cultural, or ethical limits (α1 = 0.43; α2 = 0.313; α3 = 0.32). The most common forms of transgression—attacks against the mainstream and expressions of impatience—too, are controversial ways of demonstrating authenticity, as indicated by their low intercoder reliability (α1 = 0.514; α2 = 0.317; α3 = 0.377 and α1 = 0.058; α2 = 0.1; α3 = 0.064, respectively). Such controversies are most likely to emerge when a transgressive attack seems similar to exclusionary rhetoric (J = 0.552). What counts as an attack becomes controversial when the target is excluded from the cultural bounds from within which the attack is launched. In the flux of sociality and culture, it becomes difficult to assess what counts as crossing a limit (Foucault, 1977).
We see, then, that in the news stories we studied, authenticity appears polyphonic at the unstable borders of belonging, in the contestation of expertise, and in the difficulty of fixing a transgressable limit to collective life. It is precisely this polyphony—the multiple forms that authenticity can take, even if it is algorithmically generated—that makes authenticity controversial. Authenticity need not be just about a news story’s facticity, but about its evocation of cultural familiarity or its telling of a transgressive truth.
Importantly, though, we find that the polyphony of news authenticity is itself patterned. Every feature of authenticity appears highly similar to a rhetoric of exclusion. The diverse ways in which a news story could articulate its authenticity depended on its rhetoric, locating certain people and ideas outside its cultural sphere. No matter its audience, a news story establishes its authenticity by relating its narrative to an excluded outside, an otherness. The “rawness of authenticity” does not belie its construction (Cmiel & Peters, 2020, p. 153).
What makes a news story authentic, despite the patterns of its algorithm, was not something we could reliably determine solely by identifying its formal features—its algorithmic functions. We posit that to identify what makes news coverage of anti-Asian racism controversially authentic, we need to attend to that other constitutive element of these news stories: their contents.
Critical Discourse Analysis: Findings and Discussion
To parse the polyphonic authenticity of news stories about anti-Asian racism, we studied how they articulated belonging, expertise, and transgression in relation to Asianness in, across, and beyond North America. In particular, we studied how that polyphony emerged when news stories posited a culturally excluded outside when speaking about Asianness.
News stories’ rhetoric of belonging became an unstable aspect of their authenticity when it articulated where Asian Americans and Canadians belonged: within North America or elsewhere, in Asia. In one such instance, Chinese Canadians complained they face racism even though they identified with Canada over China: “many of [us] don’t even speak the language [Chinese]” or “don’t really identify with anything in China” (Fraser, 2020, para. 21). Such rhetoric, which was seen as both inclusionary and exclusionary, was also used to describe the historical and contemporary experiences of Asian Americans and Canadians and how these experiences shaped Asian communities’ relationship to North American society (e.g., in Price, 2020; Rodriguez, 2021). The tenor of that relationship became especially divisive when describing ostensive Chinese influence on North American society and politics (e.g., Bryden, 2021; Fu, 2022). We see this, for example, in a news story mentioning reports that “Chinese Canadians . . . [were] acting on the behest of China” (Bryden, 2021, para. 20). Even as news stories placed Asian Americans and Canadians within the United States and Canada, they presented Asia—particularly China—as a cultural outside.
News stories invoked contested forms of expertise to assert knowledge of Asian entities’ experiences and actions during the pandemic, from the racism Asian Americans and Canadians faced to how China responded to the spread of COVID-19. In one such case, a news story implied that it was people of color rather than white supremacists who drove anti-Asian violence, claiming that “the suspects in Asian robbery attacks are almost exclusively, in [one] cop’s experience, black” (Donald, 2021, para. 14). Another news story about the efficacy of China’s lockdown policies during the pandemic stated that “even experts outside of China who question the medical wisdom of the policy are nevertheless impressed” (Cheng, 2021, para. 49). In these instances, expertise had to be dislocated from Asianness—from the Asian entities enacting or experiencing the object of that knowledge—to validate it.
In their transgressive moments, news stories also demonstrated their authenticity by attacking China as an actor alien to North America, accusing it of incompetence, if not outright malice, in handling the pandemic (e.g., Greenfield, 2020; Klein, 2021). For example, one news story included the accusation that “Beijing omitted [a gene] sequence from the genome it provided the world at the beginning of the outbreak” (van Laar, 2021, para. 12), implying that the Chinese government deliberately provided inaccurate scientific information about the virus to other countries. In such cases, China could be attacked because it was outside the normative bounds of the United States and Canada, constituting an other that threatened North American societies.
The polyphonic authenticity of these news stories comes from the interrelated, but contradictory, ways they relate to Asianness, overdetermined by China’s geopolitical and economic significance. On the one hand, news stories discuss Asian American and Canadian life, including anti-Asian racism, as a matter of North American concern, as something that can be circumscribed by North American sociality and culture. On the other hand, these stories unavoidably refer to Asia—particularly China—when discussing Asian Americans and Canadians.
During the pandemic, as has been the case historically, Asianness moved between—transited—North America and Asia, making it recognizable both in its “local” aspect within the United States and Canada and its threatening “foreign” aspect, which, in the news stories we examined, relates it to China. Asianness could be cast as both an inherent part of contemporary North American life and as the “yellow peril” (Cho, 2022; Siu & Chun, 2020). In having to discursively manage this contradiction, any singular image of Asianness—citizen or threat, within or without—that news stories attempt to use to establish their authenticity is exploded; it becomes ambivalent (Palumbo-Liu, 1999).
This discursive ambivalence belies the promises of North America multiculturalism, which models a form of sociality where Asian persons can be American or Canadian, but Asianness itself must be located beyond the bounds of North American life—it must remain “forever foreign” (Palumbo-Liu, 1999). As the pandemic-era resurgence of anti-Asian racism demonstrates, North American multiculturalism is limited to the social inclusion of Asian Americans and Canadians in North America. It continues to need a cultural other—within and without—that can be called on when a hegemon wants—when, for example, the United States or Canada names China a geopolitical, economic, or national (bio-)security threat (White, 2023).
There is no singular way to authentically articulate Asianness, even though news stories try to. What does remain consistent is the abjection of Asianness from Asian American and Canadian life (Shimakawa, 2002). As long as North American news remains discursively invested in multiculturalism, with its investment in a separation between “Asians in America” and “Asians in Asia” (Chuh, 2003, p. 90), its authenticity will be polyphonic, predicated on Asian otherness.
Limitations
Our investigation of authenticity in the news is limited to the specific context of reporting around anti-Asian racism during COVID-19. As noted previously, our findings indicate an algorithm of authenticity, but the controversies we observe indicate that authenticity can take different shapes across the subjects the news covers; for example, the frequencies of its constituent features might change when replicating this study outside the context of anti-Asian racism.
Furthermore, we took the controversies of authentication, indicated by intercoder dis/agreement, as a methodological problem rather than the endpoint of analysis. There may be other reasons for such disagreement, including how coders interpreted the schema, which we do not address here. While our methodological interventions could generalize to other contexts where (algorithmic) authenticity matters, the outcomes of our analysis are specific to the case of anti-Asian racism and its news coverage.
Conclusion
In this study, we examined which features of authenticity—which of its algorithmic functions—could establish the authenticity of news stories concerned with anti-Asian racism in North America. Most often, news stories rhetorically articulate where people and ideas belong, cite expertise, and describe transgressions against the status quo. At the same time, we could not reliably ascribe news authenticity to a definitive, singular arrangement of its features. Authenticity is polyphonic—it can appear in multiple complementary ways. The appearance of authenticity in a news story cannot be causally traced back—reduced—to its algorithm. This is not to say that authenticity can take arbitrarily many shapes in the news; authenticity is typically defined in relation to a cultural outside, an otherness, as we see in the fact that each of its features demonstrates high similarity to rhetorics of cultural exclusion.
In the case of news stories covering anti-Asian racism during COVID-19, their polyphonic authenticity is an effect of the interrelated, but contradictory, ways these stories relate to Asianness, overdetermined by China’s geopolitical and economic significance. They are discursively invested in North American multiculturalism, whose distinction between “Asians in America” and “Asians in Asia” (Chuh, 2003, p. 90) they cannot maintain. It is in attempting to describe that ambivalent position of Asian American and Canadian life that the authenticity of news stories becomes polyphonic.
To conclude, we cannot identify a single, universal algorithm whose execution guarantees a news story’s authenticity. There are a range of ways news stories can demonstrate their authenticity, meaning that what makes a news story authentic depends, in the final instance, on which features of authenticity someone recognizes in it. What is evident is that factual accuracy is no longer sufficient—if it ever was—to draw audiences to a news story’s truth claims. The news clearly still holds some power, some authority over how we grasp our collective reality, but to understand how, we need to understand the authenticity of news, which both depends on and exceeds notions of truth and reality. The challenge is not to find a definitive way to analyze authenticity, but to grasp its multiplicity—how it can be singular and formulaic all at once.
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Copyright © 2026 (Prem Xavier Sylvester, Sage Hughes, Matt Canute, Javier Ruiz-Soler, and Wendy Hui Kyong Chun). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at https://ijoc.org.
https://doi.org/10.65476/q6aqt363
[1] The authors are grateful to the members of the Digital Democracies Institute, participants in the panel “Performances of Authenticity and the Spread of Mis/disinformation Online,” at the 2023 conference of the Canadian Communication Association, and the anonymous reviewers for their thoughtful feedback. This article draws on research supported by the Social Sciences and Humanities Research Council of Canada, the Canada Excellence Research Chairs Program, and the Data Fluencies project.
[2] https://osf.io/ug27d/?view_only=bb1b7e3e81524f6496ee7114dd5dd5d7.
[3] These terms account for North American news coverage around anti-Asian harassment and violence between 2020 and 2022. We found that several news stories in our corpus connected African Americans to acts of discrimination and violence against Asians. By including terms used in these stories, such as “Black-on-Asian,” we sought to examine how such news stories covered interracial tensions. We discuss our findings in the qualitative analysis.
[4] Disagreement refers to 0/3 coders assigning the same feature of authenticity to a unit. Partial agreement refers to 2/3 of coders (≥ 67%) assigning the same feature. Perfect agreement refers to 3/3 coders (100%) assigning the same feature to a unit.
[5] Columns 1, 2, and 3 indicate how many coded units were assigned to each category and its features, with Column 4 enumerating which percentage of coded units in a category was assigned to each of its features. Columns 5 and 6 indicate the distribution of coded units in each category and its features across the data set.