# From Dogwhistles to Bullhorns:
Unveiling Coded Rhetoric with Language Models

Julia Mendelsohn♢ Ronan Le Bras♣ Yejin Choi♠♣ Maarten Sap♡♣
♢University of Michigan School of Information ♣Allen Institute for AI
♠Paul G. Allen School of Computer Science & Engineering, University of
Washington
♡Language Technologies Institute, Carnegie Mellon University
<EMAIL_ADDRESS>dogwhistles.allen.ai Work done while interning at the Allen
Institute for AI.

###### Abstract

Warning: content in this paper may be upsetting or offensive to some readers.

Dogwhistles are coded expressions that simultaneously convey one meaning to a
broad audience and a second one, often hateful or provocative, to a narrow in-
group; they are deployed to evade both political repercussions and algorithmic
content moderation. For example, in the sentence “we need to end the
cosmopolitan experiment,” the word “cosmopolitan” likely means “worldly” to
many, but secretly means “Jewish” to a select few. We present the first large-
scale computational investigation of dogwhistles. We develop a typology of
dogwhistles, curate the largest-to-date glossary of over 300 dogwhistles with
rich contextual information and examples, and analyze their usage in
historical U.S. politicians’ speeches. We then assess whether a large language
model (GPT-3) can identify dogwhistles and their meanings, and find that
GPT-3’s performance varies widely across types of dogwhistles and targeted
groups. Finally, we show that harmful content containing dogwhistles avoids
toxicity detection, highlighting online risks of such coded language. This
work sheds light on the theoretical and applied importance of dogwhistles in
both NLP and computational social science, and provides resources for future
research in modeling dogwhistles and mitigating their online harms.

##  1 Introduction

> The cosmopolitan elite look down on the common affections that once bound
> this nation together: things like place and national feeling and religious
> faith… The cosmopolitan agenda has driven both Left and Right…It’s time we
> ended the cosmopolitan experiment and recovered the promise of the republic.
> –Josh Hawley (R-MO), 2019

> We have got this tailspin of culture, in our inner cities in particular, of
> men not working and just generations of men not even thinking about working
> or learning to value the culture of work.
> –Paul Ryan (R-WI), 2014

Figure 1: Schematic of how dogwhistles work, based on Henderson and McCready
(2018) with the example of cosmopolitan. First, a speaker simultaneously
communicates the dogwhistle message and their persona (identity). The in-group
recovers both the message content and speaker persona, enabling them to arrive
at the coded meaning (e.g. Jewish). The out-group only recognizes the
message’s content and thus interprets it literally. This literal meaning also
provides the speaker with plausible deniability; if confronted, the speaker
can claim that they solely intended the literal meaning.

Cosmopolitan and inner city are examples of dogwhistles, expressions that
“send one message to an out-group and a second (often taboo, controversial, or
inflammatory) message to an in-group” (Henderson and McCready, 2018). Many
listeners would believe that Hawley is simply criticizing well-traveled or
worldly people, but others recognize it as an attack on the Jewish people.
Similarly, many assume that Ryan is discussing issues within a geographic
location, but others hear a pernicious stereotype of Black men as lazy.
Crucially, Hawley and Ryan can avoid alienating the out-group by maintaining
plausible deniability: they never explicitly say “Jewish” or “Black”, so they
can reject accusations of racism (Haney-López, 2014).

Because dogwhistles can bolster support for particular policies or politicians
among the in-group while avoiding social or political backlash from the out-
group, they are a powerful mechanism of political influence (Mendelberg, 2001;
Goodin and Saward, 2005). For example, racist dogwhistles such as states’
rights and law and order were part of the post-Civil Rights Republican
Southern Strategy to appeal to white Southerners, a historically Democratic
bloc (Haney-López, 2014). Despite polarization and technology that enables
message targeting to different audiences, dogwhistles are still widely used by
politicians (Haney-López, 2014; Tilley et al., 2020) and civilians in online
conversations (Bhat and Klein, 2020; Åkerlund, 2021).

Beyond political science, research on dogwhistles is urgent and essential for
NLP, but they remain a challenge to study. Dogwhistles are actively and
intentionally deployed to evade automated content moderation, especially hate
speech detection systems (Magu et al., 2017). They may also have harmful
unseen impacts in other NLP systems by infiltrating data used for pretraining
language models. However, researchers face many difficulties. First, unless
they are a part of the in-group, researchers may be completely unaware of a
dogwhistle’s existence. Second, dogwhistles’ meanings cannot be determined by
form alone, unlike most overt hateful or toxic language. Rather, their
interpretation relies on complex interplay of different factors (context,
personae, content, audience identities, etc.; Khoo, 2017; Henderson and
McCready, 2018, 2019; Lee and Kosse, 2020), as illustrated in Figure 1. Third,
since their power is derived from the differences between in-group and out-
group interpretations, dogwhistles continuously evolve in order to avoid being
noticed by the out-group.

We establish foundations for large-scale computational study of dogwhistles by
developing theory, providing resources, and empirically analyzing dogwhistles
in several NLP systems. Prior work largely focuses on underlying mechanisms or
political effects of dogwhistle communication (Albertson, 2015; Henderson and
McCready, 2018) and typically considers a very small number of dogwhistles
(often just one). To aid larger-scale efforts, we first create a new taxonomy
that highlights both the systematicity and wide variation in kinds of
dogwhistles (§2.1). This taxonomy characterizes dogwhistles based on their
covert meanings, style and register, and the personae signaled by their users.
We then compile a glossary of 340 dogwhistles, each of which is labeled with
our taxonomy, rich contextual information, explanations, and real-world
examples with source links (§2.2-2.3). As this glossary is the first of its
kind, we highlight its value with a case study of racial dogwhistles in
historical U.S. Congressional Speeches (§3).

We then apply our taxonomy and glossary to investigate how dogwhistles
interact with existing NLP systems (§4). Specifically, we evaluate the ability
of large language models (i.e. GPT-3) to retrieve potential dogwhistles and
identify their covert meanings. We find that GPT-3 has a limited capacity to
recognize dogwhistles, and performance varies widely based on taxonomic
features and prompt constructions; for example, GPT-3 is much worse at
recognizing transphobic dogwhistles than racist ones. Finally, we show that
hateful messages with standard group labels (e.g. Jewish) replaced with
dogwhistles (e.g. cosmopolitan) are consistently rated as far less toxic by a
commercially deployed toxicity detection system (Perspective API), and such
vulnerabilities can exacerbate online harms against marginalized groups (§5).

This work highlights the significance of dogwhistles for NLP and computational
social science, and offers resources for further research in recognizing
dogwhistles and reducing their harmful impacts. Our glossary, code, results,
GPT-3 outputs, and a form for adding new dogwhistles to our glossary are all
available at: https://dogwhistles.allen.ai.

Figure 2: Visual hierarchical representation of our dogwhistle taxonomy along
with examples of each type.

##  2 Curating a dogwhistle glossary

###  2.1 Taxonomy

Based on prior work and our own investigations, we craft a new taxonomy
(Figure 2). We categorize dogwhistles by register, type, and persona.

#### Register

We label all dogwhistles as either part of a formal/offline or informal/online
register. Formal/offline dogwhistles originated in offline contexts or are
likely to appear in statements by mainstream political elites (e.g. family
values). The informal/online register includes dogwhistles that originated on
the internet and are unlikely to be used in political speech (e.g.
cuckservative).

#### Type I

Henderson and McCready (2018) distinguish dogwhistles into two types: Type I
dogwhistles covertly signal the speaker’s persona but do not alter the
implicatures of the message itself, while Type II dogwhistles additionally
alter the message’s implied meaning. We extend this typology to highlight the
wide variety of dogwhistles, which has important consequences for building a
theory of dogwhistles as well as future computational modeling. We identify
three subcategories of “only persona-signaling” (Type I) dogwhistles: symbols
(including emojis, abbreviations, and imagery), self-referential terms for
members of the in-group, and dogwhistles that require specialized knowledge
from a shared in-group culture.

#### Type II

Dogwhistles with an “added message meaning” (Type II) tend to fall into two
subcategories: they name a concept or serve as a substitute for a target group
label. We further divide concepts into policies (titles for initiatives with
covert implications, such as law and order), values that the in-group purports
to uphold, expressions whose covert meanings are grounded in in-group humor,
and other concepts, which are often coded names for entities that are not
group labels (e.g. the New World Order conspiracy theory is antisemitic but
does not name or describe Jewish people).

Dogwhistles serve as target group labels in three ways. Many are stereotype-
based, whose interpretations rely on pre-existing associations between the
dogwhistle and target group; we separate these into stereotype-based target
group labels, which directly name the target group (e.g. cosmopolitan), while
stereotype-based descriptors are less direct but still refer to the target
group (e.g. inner-city). Others have an arbitrary or phonetic relationship to
the group label; these are commonly used to evade content moderation, such as
“Operation Google” terms invented by white supremacists on 4chan to replace
various slurs (Magu et al., 2017; Bhat and Klein, 2020). The final
subcategory, Bogeyman, includes names of people or institutions taken to
represent the target group (e.g. George Soros$\leftrightarrow$Jewish, or
Willie Horton$\leftrightarrow$Black).

#### Persona

Persona refers to the in-group identity signalled by the dogwhistle. Figure 2
lists some personae, but this is an open class with many potential in-groups.
There is considerable overlap in membership of listed in-groups (e.g. white
supremacists are often antisemitic), so we label persona based directly on
explanations from sources referenced in our glossary (as described in 2.2).
Drawing upon third-wave sociolinguistics, personae are not static labels or
stereotypes; rather, people actively construct and communicate personae
through linguistic resources, such as dogwhistles (Eckert, 2008).

###  2.2 Gathering dogwhistles

We draw from academic literature, media coverage, blogs, and community-sourced
wikis about dogwhistles, implicit appeals, and coded language. Since academic
literature tends to focus on a small set of examples, we expanded our search
to media coverage that identifies dogwhistles in recent political campaigns
and speeches (e.g. Burack, 2020) or attempts to expose code words in hateful
online communities (e.g. Caffier, 2017). During our search, we found several
community-sourced wikis that provided numerous examples of dogwhistles,
particularly the RationalWiki “Alt-right glossary”, “TERF glossary”, and “Code
word” pages.111rationalwiki.org/wiki/{Alt-
right_glossary,TERF_glossary,Code_word}

Dogwhistle | Sex-based rights
---|---
| In-group
---
meaning
Trans people threaten cis women’s rights
Persona | Transphobic
Type | Concept: Value
Register | Formal
Explanation |  | Many anti-transgender people [claim that] women’s “sex-based
---
rights” are somehow being threatened, removed, weakened,
eroded, or erased by transgender rights… “Sex-based rights”,
by the plain English meaning of those words, cannot exist in a
country that has equality law… it’s mostly a dog-whistle: a
rallying slogan much like “family values” for religious
conservatives, which sounds wholesome but is a deniable and
slippery code-word for a whole raft of unpleasant bigotry.
Source | Medium post by David Allsopp
Example |  | When so-called leftists like @lloyd_rm demand that we give up
---
our hard-won sex-based rights, they align themselves squarely
with men’s rights activists. To both groups, female trauma is
white noise, an irrelevance, or else exaggerated or invented.
Context | Tweet by J.K. Rowling on June 28, 2020

Table 1: Example glossary entry for the transphobic dogwhistle sex-based
rights

###  2.3 Glossary contents

Our glossary contains 340 English-language dogwhistles and over 1,000 surface
forms (morphological variants and closely-related terms), mostly from the U.S.
context. Each dogwhistle is labeled with its register, type, and signaled
persona, an explanation from a linked source, and at least one example with
linguistic, speaker, situational, and temporal context included, as well as a
link to the example text. Table 1 shows one glossary entry for the transphobic
dogwhistle sex-based rights.

Antisemitic, transphobic, and racist (mostly anti-Black but sometimes
generally against people of color) dogwhistles are the most common, with over
70 entries for each persona. The glossary includes dogwhistles with other
personae, such as homophobic, anti-Latinx, Islamophobic, anti-vax, and
religious. See Table A.1 in the Appendix for glossary statistics across
register, type, and persona. Because dogwhistles continuously evolve, we
intend for this resource to be a living glossary and invite the public to
submit new entries or examples.

##  3 Case study: racial dogwhistles in historical U.S. Congressional speeches

We showcase the usefulness of our glossary, with a diachronic case study of
racial dogwhistles in politicians’ speeches from the U.S. Congressional Record
(Gentzkow et al., 2019; Card et al., 2022) to analyze the frequency of
speeches containing racist dogwhistles from 1920-2020. For this case study, we
simply identify glossary terms based on regular expressions and do not
distinguish between covert and literal meanings of the same expressions. We
also measure how ideologies of speakers using dogwhistles changed over time
using DW-NOMINATE (Poole and Rosenthal, 1985), a scaling procedure that places
politicians on a two dimensional map based on roll call voting records, such
that ideologically similar politicians are located near each other (Carroll et
al., 2009; Lewis et al., 2023). We consider the first dimension of DW-
NOMINATE, which corresponds to a liberal-conservative axis.222The second
dimension captures salient cross-cutting issues, and some argue that this
dimension primarily captures race relations (Poole and Rosenthal, 1985).
However, the second dimension’s interpretation is less clear as the vast
majority of voting variation is along the first dimension, and is often
ignored by political scientists (Bateman and Lapinski, 2016). We thus restrict
this case study to the first dimension though future work may opt to consider
the second dimension as well.

As shown in Figure 3, dogwhistle use began to increase during the Civil Rights
Era, following the 1954 Brown vs. Board of Education Supreme Court decision
mandating racial integration of public schools. This aligns with qualitative
accounts of the Republican Southern Strategy: because explicit racism was no
longer acceptable, politicians turned to dogwhistles to make the same appeals
implicitly, particularly aiming to gain the support of white voters in the
Southern United States (Mendelberg, 2001). Their frequency continued to
increase from the 1970s through the 1990s, paralleling Haney-López (2014)’s
account of dogwhistles during the Nixon, Reagan, Bush Sr., and Clinton
presidencies. Since the 1990s, the frequency of racial dogwhistles has
fluctuated but remained high. Like Haney-López (2014), we qualitatively
observe that the dogwhistles invoked post-9/11 have shifted towards being more
Islamophobic and anti-Latinx rather than exclusively anti-Black. We caution
that this case study and Figure 3 do not make novel claims; rather, our goal
is to show that even a naive application of our glossary illustrates
qualitatively well-established historical patterns in U.S. politics.

Figure 3: Frequency of speeches containing racial dogwhistles in the U.S.
Congressional Record (as a fraction of total speeches) over time. The dotted
red vertical lines represent noteworthy years. Use of racial dogwhistles began
to increase during the Civil Rights Movement and their frequency continued to
rise until the 1990s. Since the 1990s, the frequency of speeches containing
dogwhistles has fluctuated but remained at overall high levels compared to
earlier years. Figure 4: Average ideology score (DW-NOMINATE first dimension)
for speakers who used selected dogwhistles over time: welfare reform (top
left), thug (top right), property rights (bottom left), and Willie Horton
(bottom right). Higher values indicate that the dogwhistle’s speakers were
more conservative, while lower values indicate that the dogwhistle’s speakers
were more liberal. For visualization, trends are Lowess-smoothed.

Figure 4 shows how the average ideologies of speakers who use particular
dogwhistles (property rights, thug, welfare reform, hardworking Americans, and
Willie Horton) have shifted over time, and reveals interesting insights into
the evolution and lifecycle of dogwhistles. Most racial dogwhistles in the
U.S. Congressional Speeches have become increasingly associated with more
conservative speakers over time. However, the inflection point when speaker
ideologies shift varies across dogwhistles, suggesting that they emerged as
dogwhistles at different points. For example, property rights became
increasingly associated with more conservative speakers since the 1960s, while
the average ideology of speakers using welfare reform did not change until the
1990s.

Willie Horton presents an interesting example. In his 1988 presidential
campaign, George Bush ran a television advertisement featuring Willie Horton,
a Black man convicted of rape and murder while on prison furlough (Mendelberg,
2001). The ad was so powerful among white voters that it propelled Bush to
victory, but shortly afterwards was explicitly called out as racist (Haney-
López, 2014). We see this pattern in Figure 4: in 1988, Willie Horton was
associated with extremely conservative speakers, but quickly became more
liberal, and Willie Horton no longer functioned as a successful dogwhistle.

##  4 Recognition of dogwhistles in GPT-3

We conduct two experiments to assess if a large language model, GPT-3 (Brown
et al., 2020), can recognize dogwhistles. First, we interrogate whether GPT-3
is able to identify covert meanings of dogwhistles from our glossary, an
ability that would be instrumental in understanding the breadth of online
bigotry. Second, we measure GPT-3’s ability to surface dogwhistles, motivated
by the fact that dogwhistles are often intentionally obscured from researchers
which makes it impossible to ensure that a manual search is complete or
comprehensive. Since GPT-3 is trained on large portions of internet data, it
may be able to reveal a more comprehensive and diverse set of dogwhistles than
manual efforts. Finally, we present a small manual analysis of in-context
dogwhistle recognition using our glossary examples and GPT-4 as a proof of
concept for future work.

###  4.1 Identifying covert meanings

To gauge which types of in-group language GPT-3 might have seen during
training, we examine if it is able to identify dogwhistles’ covert meanings.

#### Experimental setup

We create prompts that vary in (1) which of the five definitions of
dogwhistles is provided, if any, and (2) if the prompt includes a secret cue.
For each input term, we construct 12 prompts (6 definitions including none,
and each with or without a secret cue). Consider the example below, with a
definition, secret cue, dogwhistle and GPT-3’s response. Prompts with no
secret clue simply exclude the word secretly. We generate responses for each
dogwhistle in the glossary and consider multiple forms for some, such as
actual emojis as well as descriptions. We thus test 480 variants with 12
prompts for each, leading to 5,760 generations.333We use the text-davinci-002
GPT-3 model with max_tokens=256, temperature=0.0 (more deterministic outputs)
and num_outputs=1.

> A dogwhistle is the use of coded or suggestive language in political
> messaging to garner support from a particular group without provoking
> opposition. For example, “cosmopolitan” secretly means “Jewish to many anti-
> Semitic people.

| No Secret Cue | With Secret Cue
---|---|---
No Definition | .085 [±.025] | .296 [±.021]
With Definition | .437 [±.010] | .543 [±.010]

Table 2: Proportion of dogwhistles with covert meanings identified by GPT-3
when prompted with a dogwhistle definition, a secret cue, both, or neither.
Confidence intervals are shown in brackets.

#### Results

The first author manually evaluated whether or not each of the 5,760 GPT-3
generations contains the covert meaning for each dogwhistle. 80.3% of
dogwhistles had their covert meanings identified in at least one generation.
Overall, 56.0% generations contained the correct covert meaning for
dogwhistles that are part of the formal/offline register, but just 29.4% for
dogwhistles in the informal/online register. We refer readers to Appendix A.2
(Figure A.2) for more details about register-based variation and examples of
dogwhistles for which GPT-3 performed particularly well or poorly.

The specific prompt form strongly impacts GPT-3’s ability to generate covert
meanings (Table 2). Without a definition or secret cue, covert meanings are
identified in just 8.5% of generations. Including both a definition and secret
cue improves GPT-3’s performance over 5-fold, with dogwhistles’ covert
meanings identified in 54.3% of generations.

We observe wide variation in GPT-3’s ability to identify covert meanings
across personae. Among the most represented personae in our glossary (at least
100 generations for each), GPT-3 has the lowest recognition of transphobic
dogwhistles, the highest recognition of homophobic and Islamophobic
dogwhistles, with antisemitic, white supremacist, and racist dogwhistles in
the middle (Appendix Table A.3). There is also variation in performance by
dogwhistle type and the specific definition provided; we refer the reader to
Appendix A.2 and Figure A.3 for more details.

###  4.2 Surfacing dogwhistles

In addition to evaluating if GPT-3 can identify dogwhistles’ covert meanings,
we assess GPT-3’s ability to surface dogwhistles in text generation.

#### Experimental setup

We construct a series of prompts that begin with one of five definitions of
dogwhistles from prior work (Table A.2). The definition is followed by a
question or request for examples (see Appendix A.1 for more prompting
details). In the following example, the definition is marked in blue, the
request in purple, and GPT-3’s response is highlighted in yellow.

> A dogwhistle is the use of coded or suggestive language in political
> messaging to garner support from a particular group without provoking
> opposition. What are examples of dogwhistles?
> 1\. “Law and order”
> 2\. “The silent majority”
> 3\. “Family values”
> 4\. “Welfare queens”
> 5\. “Illegal aliens”

#### Evaluation

We use our glossary as a proxy to measure precision and recall of GPT-3’s
ability to surface dogwhistles because an exhaustive ground-truth set of
dogwhistles does not exist. We calculate recall as the proportion of
dogwhistles in our glossary that were also surfaced at least once by GPT-3.
For precision, the authors manually inspect candidates appearing in at least
4% of GPT-3 text generations for generic, white supremacist, racist,
antisemitic, Islamophobic, and transphobic prompt types. Because our glossary
is not exhaustive, this method yields conservative estimates (see Appendix A.1
for more evaluation details).

#### Precision Results

We find that GPT-3 does have the ability to surface dogwhistles when prompted
to do so, but caution that such results are imperfect and require manual
verification. The most common errors involve explicit mentions of groups in
stereotypes or conspiracy theories (Jews are behind the 9/11 attacks) or
phrases that may accompany dogwhistles but are not dogwhistles themselves (I’m
not racist but…). Precision in dogwhistle surfacing varies across prompt
types; while the average precision over all six prompt types is 66.8%, scores
range from just 50% for transphobic dogwhistle prompts to 91.3% for generic
prompts (Figure A.1).

Figure 5: Recall of GPT-3 dogwhistle surfacing separated by persona and
register. Across all personae, GPT-3 surfaces under 20% of dogwhistles in the
informal/online register. Performance is much higher for the formal/offline
register but varies across personae, ranging from 44.8% (transphobic) to 100%
(Islamophobic).

#### Recall Results

GPT-3 surfaced 153 of 340 dogwhistles in our glossary (45%). We observe
significant differences by register: GPT-3 surfaced 69.4% of formal/offline
dogwhistles but just 12.9% of informal/online dogwhistles. Despite its ability
to generate emojis and other symbols, GPT-3 did not surface any symbols or
emojis from our glossary except for the antisemitic triple parentheses
“((()))”.

Figure 5 shows GPT-3 surfacing recall results by both register and in-group
personae. We show results for the five most-frequent personae represented in
our glossary. Recall of dogwhistles in the informal/online register is low
across the board. For the formal/offline register, recall is considerably
higher although it varies widely across personae. As with precision, GPT-3 has
the lowest performance for transphobic dogwhistles, surfacing just 44.8% of
formal/offline transphobic dogwhistles. For formal/offline antisemitic
dogwhistles, recall is considerably higher but far from perfect at 71.7%.
GPT-3 has 80.3% and 83.3% recall of racist and white supremacist dogwhistles,
respectively, and full 100% recall of Islamophobic dogwhistles.

###  4.3 Identifying dogwhistles in context

Our experiments show that LLMs have some ability to identify covert meanings
of a given dogwhistle specified in the prompt (Section 4.1) and generate
dogwhistles (Section 4.2). We now consider a natural follow-up question: can
LLMs correctly identify dogwhistles in real-world texts?

Fully addressing this question would require a larger dataset including a
variety of linguistic contexts per dogwhistle, documents containing no
dogwhistles, and documents with dogwhistle expressions that do not carry the
dogwhistle meaning. We leave such a rigorous analysis for future work. For
now, we present a small manual analysis with ChatGPT/GPT-4 (OpenAI, 2023),
using several real-world examples from our glossary as a proof of concept for
in-context dogwhistle recognition.

We prompt GPT-4 with the instructions below. Appendix Table A.5 shows the
exact glossary example and GPT-4 output text for the dogwhistles cosmopolitan
(elite) (antisemitic), inner-city (racist), #IStandWithJKRowling,
(transphobic), and did you see Kyle? (white supremacist).

> A dogwhistle is the use of coded or suggestive language in political
> messaging to garner support from a particular group without provoking
> opposition. Identify the dogwhistle in the following message: [GLOSSARY
> EXAMPLE].

Using the examples presented in Section 1, GPT-4 correctly identifies the
dogwhistles cosmopolitan (elite) and inner city, and generates a correct
explanation for their covert meanings. However, the model does not work as
well for the other examples. For the example containing #IStandWithJKRowling,
GPT-4 correctly identifies that text covertly signals transphobia through
support of JK Rowling, but does not select this hashtag as the dogwhistle. On
the other hand, GPT-4 correctly identifies the dogwhistle  in a tweet from JK
Rowling, and correctly relates this symbol to the women’s suffrage movement,
but does not capture the appropriation of this symbol to covertly communicate
transphobia. Finally, GPT-4 misses both the dogwhistle and the precise covert
meaning for did you see Kyle? (“see Kyle” sounds similar to the Nazi slogan
“Sieg Heil”); while the model still ultimately identifies covert white
supremacy, it generates a false explanation connecting the glossary example to
this persona.

##  5 Dogwhistles and toxicity detection

Beyond evaluating language models’ ability to recognize dogwhistles, we seek
to understand how dogwhistles affect the decisions that NLP systems make, and
how this has downstream implications for content moderation and online safety.
We begin to address this with a study of how dogwhistles are handled by a
widely-deployed toxic language detection system, Google/Jigsaw’s Perspective
API.444https://perspectiveapi.com/ Perspective API scores a text between 0 and
1 for a range of attributes (e.g. toxicity, identity attack, profanity),
representing the estimated probability that a reader would perceive the text
to contain that attribute. Perspective API’s models are multilingual BERT-
based models distilled into single-language convolutional neural networks for
faster inference, and are trained on annotated data from online forums. We
refer readers to the Perspective API Model Cards for more
details.555https://developers.perspectiveapi.com/s/about-the-api-model-cards

#### Experimental setup

We consider 237 hateful sentence templates from HateCheck (Röttger et al.,
2021), a test suite for bias in hate speech detection, that contain
placeholders for identity terms (group referents) in either adjectival,
singular nominal, or plural nominal forms. We fill filled with a standard
group label, a slur, or a dogwhistle in the corresponding grammatical form
requested by the template. For this experiment, we consider racist (mostly
anti-Black), antisemitic, and transphobic terms, as these personae are the
most common in our glossary (see Tables A.7 and A.8 for a sample of sentence
templates and group label terms, respectively). We feed our resulting 7,665
sentences to Perspective API to get scores for toxicity, severe toxicity, and
identity attack.

Figure 6: Average Perspective API toxicity, severe toxicity, and identity
attack scores for HateCheck template sentences filled in with dogwhistles,
slurs, or standard group labels.

#### Results

Hateful sentences are rated as less toxic, less severely toxic, and less
identity-attacking when dogwhistles are used instead of standard group labels
or slurs (Figure 6). This pattern holds for all three personae (Appendix
Figure A.4).

Interestingly, mean toxicity scores for slurs are lower than for standard
group labels, especially for antisemitic slurs. We observe relatively wide
variation in Perspective API’s ratings depending on the specific choice of
slur. For example, sentences containing the N-word are almost always rated as
more toxic than the same sentences containing Black or Black people. Lower
toxicity ratings for other slurs, such as the highly derogatory antisemitic
K-word666https://ajc.org/translatehate/kike may be because, similar to
dogwhistles, Perspective API does not recognize that these terms refer to
identity groups. However, deeper analysis of slurs is outside the scope of the
current work.

##  6 Discussion & Conclusion

We lay the groundwork for NLP and computational social science research on
dogwhistles by developing a new taxonomy and glossary with rich contextual
information and examples. We demonstrate our glossary’s utility in a case
study of historical U.S. Congressional speeches, where our quantitative
analysis aligns closely with historical accounts. We further use our glossary
to show that GPT-3 has some, but limited, ability to retrieve dogwhistles and
recognize their covert meanings. Finally, we verify that dogwhistles readily
evade PerspectiveAPI’s toxicity detection. We now turn to several implications
of this work, highlighting potential future directions across disciplines.

Dogwhistles and toxic language  Dogwhistles are closely related to other forms
of subtle biases studied in NLP, such as implicit hate speech and symbols
(Magu et al., 2017; Magu and Luo, 2018; ElSherief et al., 2018, 2021; Qian et
al., 2019; Caselli et al., 2020; Menini et al., 2021; Arviv et al., 2021;
Botelho et al., 2021; Wiegand et al., 2021a, b; Hartvigsen et al., 2022),
microaggressions (Breitfeller et al., 2019), dehumanization (Mendelsohn et
al., 2020), propaganda (Da San Martino et al., 2020), condescension (Pérez-
Almendros et al., 2020), and stereotypes (Nangia et al., 2020; Sap et al.,
2020; Nadeem et al., 2021).

However, dogwhistles are distinct from toxic language in several important
ways. First, although often implicitly abusive, they are not exclusively
hateful; for example, wonder-working power covertly signals the speaker’s
Evangelical Christian identity (Albertson, 2015). Second, dogwhistles are
characterized by dual meanings, wherein different sub-audiences interpret the
exact same message differently (Henderson and McCready, 2018). Third,
dogwhistles’ true meanings are intentionally hidden from the out-group (Saul,
2018). Nevertheless, because dogwhistles are often deployed specifically to
avoid hate speech detection and other content moderation tools, NLP
researchers should consider how dogwhistles highlight a vulnerability in
extant language technologies, which ultimately puts people’s safety and well-
being at risk.

We show that hateful speech using dogwhistles evades toxicity detection, and
is one way that NLP systems (unintentionally) perpetuate harms against
marginalized groups. This finding is not surprising, as prior work shows that
toxicity detection often fails on subtle language (Han and Tsvetkov, 2020;
Hartvigsen et al., 2022), but underscores the need for toxicity and hate
speech detection models to be able to flag hateful dogwhistles. One potential
approach to improve such models could be to train them to recognize
dogwhistles in naturally-occurring in-group contexts (starting with modeling
contextual factors; Zhou et al., 2023). More broadly, content moderation
pipelines should take context into account and consider mechanisms to identify
when a dogwhistle has potentially negative consequences. Beyond toxicity
detection, future work ought to consider the impact of dogwhistles in a
broader range of NLP tasks, such as bias mitigation or story generation.

How do LLMs know about dogwhistles?  Our findings regarding GPT-3’s ability to
surface and identify dogwhistles’ covert meanings are probably driven by the
contents of the training data. GPT-3’s training data likely includes right-
wing extremist content, as has been shown with its predecessor GPT-2 (Gehman
et al., 2020), which may result in high performance for dogwhistles from these
in-groups. Or perhaps the model is simply memorizing articles or social media
posts that explicitly call out certain expressions as dogwhistles. Future work
could evaluate if large language models can learn dogwhistles’ covert meanings
from in-context usage alone by experimentally controlling for whether or not
these terms are explicitly exposed as dogwhistles in the training data.

Moreover, we find that GPT-3’s performance varies widely across target groups.
Transphobic dogwhistles are notably difficult for GPT-3 to surface and
identify. Perhaps this is because the model is trained on fewer data from
transphobic communities compared to other in-groups considered in this work.
Furthermore, transphobic dogwhistles may be less frequent in the training data
because many have emerged relatively recently. Another reason may be
formatting: transphobic dogwhistles are often emoji-based and appear in social
media screen names and profile bios rather than in posts themselves. We hope
that future work will investigate the links between language models’ knowledge
of dogwhistles and training data.

Potential of LLMs for dogwhistle research  Beyond the risks presented by
current NLP technologies, we wish to highlight the potential benefits of using
NLP to advance dogwhistle research. Even though LLMs’ performance is likely
due to vast training data, and even then, their outputs require manual
verification, our experiments with GPT-3 demonstrate that LLMs have some
ability to surface dogwhistles and explain their covert meanings. This is
particularly valuable as dogwhistles are intentionally hidden from out-group
members, and out-group researchers may have no other way to access this
information. There is thus a unique opportunity for LLMs to assist dogwhistle
research, and political content analysis more broadly.

Bridging large-scale analysis and mathematical models  Our work builds
foundations for large-scale computational analysis of dogwhistles in real-
world political discourse. We diverge from prior quantitative dogwhistle
research, which focuses on mathematically modeling the process underlying
dogwhistle communication using probabilistic, game-theoretic, deep learning,
and network-based approaches on simulation data (Smaldino et al., 2018;
Dénigot and Burnett, 2020; Henderson and McCready, 2020; Breitholtz and
Cooper, 2021; Smaldino and Turner, 2021; Xu et al., 2021; Hertzberg et al.,
2022; van der Does et al., 2022). We are optimistic about future research
synthesizing these two strands of work to address many of the challenges
presented by dogwhistles. For example, future work could use our resources
along with these mathematical models to develop systems that can automatically
detect dogwhistle usages, emergence of new dogwhistles, or decline of older
terms as dogwhistles due to out-group awareness.

Implications for social science research  Understanding dogwhistles at scale
has vast implications across disciplines, so we develop resources useful for
both NLP and social science researchers. We provide the most comprehensive-to-
date glossary of dogwhistles and demonstrate through our case study how this
resource can be used to analyze political speeches and other corpora, such as
social media posts and newspaper articles. Dogwhistles have mostly been
studied using primarily qualitative methods (Moshin, 2018; Åkerlund, 2021) and
experiments (Albertson, 2015; Wetts and Willer, 2019; Thompson and Busby,
2021), and we hope that by facilitating quantitative content analysis, our
resources can add to dogwhistle researchers’ methodological repertoires.

##  7 Limitations

This work represents an initial push to bring dogwhistles to the forefront of
NLP and computational social science research, and as such, has many
limitations. Our glossary is the most comprehensive resource to date (to the
best of our knowledge) but aims to document a moving target, as dogwhistles
continuously emerge or fall out of use due to out-group awareness. We aim to
make this resource a “living glossary” and encourage others to submit new
entries or examples. We further encourage future research to develop models to
automatically detect the emergence of new dogwhistles.

Another major limitation in this work is that we identify as out-group members
for nearly all dogwhistles in the glossary and have an adversarial
relationship with many of the communities studied (e.g. white supremacists).
Although our work would ideally be validated by members of the in-groups, they
have very little incentive to share this information, as that would damage the
dogwhistle’s utility as a tool for covert in-group communication.

This work, like most prior work, is limited in that we operationalize
dogwhistles as a static binary; we assume each term either does or does not
have a dogwhistle interpretation and is categorically included or excluded
from our glossary and analyses. In reality, dogwhistles are far more
complicated constructs. For example, Lee and Kosse (2020) characterize
dogwhistles along two dimensions: the size of their in-group and the degree to
which their usage is conventionalized. Other axes of variation may include the
level of out-group awareness, and the social and political risks of backlash
to the communicator if the dogwhistle interpretation is exposed. It is even
possible that audience members who hear a dogwhistle further recirculate it
even if they themselves do not recognize the covert meaning (Saul, 2018). We
hope future work will consider multifaceted and continuous measures of
“dogwhistleness" that account for such nuances.

Finally, the current work is limited in the scope of dogwhistles considered:
they are all in English with the vast majority coming from the U.S. political
and cultural contexts. However, dogwhistles are prominent across cultures (Pal
et al., 2018; Åkerlund, 2021) and we hope that future work will consider other
languages and cultures, especially involving researchers who have high
awareness of or expertise in non-U.S political environments.

##  8 Ethical Implications

We caution readers about several potential ethical risks of this work. First
is the risk of readers misusing or misunderstanding our glossary. We emphasize
that dogwhistles are extremely context-dependent, and most terms in the
glossary have benign literal meanings that may be more common than the covert
dogwhistle meanings. For example, many entities from the financial sector have
been used as antisemitic dogwhistles (e.g. the Federal Reserve, bankers) but
their primary usage has no antisemitic connotations.

Relatedly, some glossary entries include terms that originate from the target
group but were appropriated by the dogwhistles’ in-group. Examples include the
appropriation of goy (a Yiddish word for non-Jewish people) as an antisemitic
in-group signal, and baby mama (originally from African American English) as a
racist dogwhistle. As with hate speech detection (Sap et al., 2019), there is
a risk of social bias in dogwhistle detection.

As we have discussed throughout this work, dogwhistle researchers face a
challenge with no exhaustive ground truth and an unknown search space. We
anticipate our glossary being a helpful resource for this reason, but because
we also lack such exhaustive ground truth, there are bound to be biases in the
representation of dogwhistles in our glossary. The current version of the
glossary may exclude groups and thus lead to worse performance in dogwhistle
detection, toxic language detection, and other downstream NLP tasks.

Our glossary also includes real-world examples of how each dogwhistle is used.
This presents a privacy risk, which we mitigate by prioritizing examples from
public figures or examples from anonymous social media accounts whenever
possible. We do not release personal information of any speaker who is not a
well-known public figure.

Finally, we do not pursue any computational modeling or prediction of
dogwhistle usages in this work, but see it as a natural direction for future
work. However, we caution researchers to consider dual-use issues in doing so.
Many people use coded language in order to avoid censorship from authoritarian
regimes (Yang, 2016) and marginalized groups may also use coded language for
their own safety (Queen, 2007). When building computational models, we urge
researchers to mitigate this dual-use risk as much as possible.

## Acknowledgements

We thank Ceren Budak, Yulia Tsvetkov, and audiences at Text as Data 2022
(TADA) and New Ways of Analyzing Variation 50 (NWAV) for their helpful
feedback on an earlier version of this work. We also thank the anonymous
reviewers for their comments and suggestions. J.M. gratefully acknowledges
support from the Google PhD Fellowship.

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##  Appendix A Appendix

| Category | Count
---|---|---
| formal/offline | 193
Register | informal/online | 147
| stereotype-based target group label | 64
| concept (policy) | 41
| concept (values) | 37
| persona signal (symbol) | 35
| stereotype-based descriptor | 34
| persona signal (self-referential) | 32
| concept (other) | 29
| arbitrary target group label | 23
| persona signal (shared culture) | 18
| humor/mockery/sarcasm | 11
| representative (Bogeyman) | 10
| phonetic-based target group label | 4
Type | persona signal (in-group label) | 2
| racist | 76
| transphobic | 73
| antisemitic | 73
| white supremacist | 48
| Islamophobic | 16
| conservative | 8
| anti-liberal | 7
| anti-Latino | 6
| homophobic | 6
| anti-vax | 5
| religious | 4
| climate change denier | 4
| anti-Asian | 3
| anti-LGBTQ | 3
| liberal | 3
| xenophobic | 2
| anti-GMO | 2
Persona | misogynistic | 1

Table A.1: Distribution of glossary entries across all registers, types, and
personae.

Source | Definition
---|---
Albertson (2015) | A dogwhistle is an expression that has different meanings to different audiences.
Henderson and McCready (2018) |  | A dogwhistle is a term that sends one message to an outgroup while
---
at the same time sending a second (often taboo, controversial, or
inflammatory) message to an ingroup.
Bhat and Klein (2020) |  | A dogwhistle is a word or phrase that means one thing to the public
---
at large, but that carry an additional, implicit meaning only recognized
by a specific subset of the audience.
Merriam-Webster |  | A dogwhistle is a coded message communicated through words or phrases
---
commonly understood by a particular group of people, but not by others.
Wikipedia |  | A dogwhistle is the use of coded or suggestive language in political messaging
---
to garner support from a particular group without provoking opposition.

Table A.2: Definitions of dogwhistles and their sources used for prompting
GPT-3.
Below are links for the Merriam-Webster and Wikipedia sources:
https://www.merriam-webster.com/words-at-play/dog-whistle-political-meaning
https://en.wikipedia.org/wiki/Dog_whistle_(politics) Figure A.1: Precision of
GPT-3 dogwhistle surfacing by prompt type. Precision was highest for
dogwhistles that were commonly surfaced in response to generic prompts, and
lowest for dogwhistles that were commonly surfaced in response to prompts
requesting examples of Islamophobic or transphobic dogwhistles.

###  A.1 Details for dogwhistle surfacing

We create 51 total request formulations that ask for generic examples of
dogwhistles (n=17), dogwhistles that target specific social groups (n=25), and
dogwhistles that are used by certain personae/in-groups (n=9). For each
prompt, we also consider three spelling variations of “dogwhistle”:
dogwhistle, dog-whistle, and dog whistle. Exact prompt text can be found in
our project repository.

To encourage GPT-3 to generate a list, we conclude all prompts with a newline
token followed by “1.”. All prompts were provided to a GPT-3 Instruct model
(text-davinci-002) with default hyperparameters except for max_tokens=$256$,
temperature=$0.7$, and num_outputs=$5$ (5 generations per prompt). The
resulting texts are strings that take the form of an enumerated list. To
aggregate and compare surfaced dogwhistles across each text completion, we
post-process by: splitting by newline characters, removing enumeration and
other punctuation, converting all outputs to lowercase, lemmatizing each
surfaced term with SpaCy, and removing definite articles that precede
generated dogwhistles. We then aggregate over all generations to determine how
often each dogwhistle is surfaced for each in-group.

In calculating precision of dogwhistle surfacing, we mark each of the 154
candidate terms as true positives if they appear in the glossary. Some
surfaced dogwhistles were marked as “correct” if they were closely related to
a dogwhistle entry in our glossary, even if the exact term did not appear.
Examples include national security, identity politics, the swamp, tax relief,
and patriot. However, this is still a conservative estimate because our
glossary is not exhaustive. GPT-3 surfaces a number of terms that potentially
have dogwhistle usages but were not covered by our glossary, and thus not
included in our precision estimates. Examples of these terms include names of
Muslim political organizations (Hezbollah, Hamas, Muslim Brotherhood) and
Second Amendment rights. Figure A.1 shows variation in precision of dogwhistle
surfacing across prompt types (in-groups and generic prompting).

###  A.2 Details for identifying covert meaning

#### Variation across registers

We identify variation in GPT-3’s ability to identify dogwhistles’ covert
meanings based on prompt features, dogwhistle register, and the interaction
between the two. Figure A.2 shows that including the definition in prompts
consistently improves GPT-3’s covert meaning identification for both formal
and informal dogwhistles. However, including the secret cue has minimal effect
for informal dogwhistles, and only leads to substantial improvement for
identifying formal dogwhistles’ covert meanings.

Figure A.2: Percent of GPT-3 generations that capture dogwhistles’ covert
meanings, separated by register and if the prompt includes a definition or
secret cue.

#### Variation across personae

There is significant variation in GPT-3’s performance across personae, as can
be seen in Table A.3.

#### Variation across dogwhistle types

GPT-3’s performance varies widely across dogwhistle types in our taxonomy
(§2.1; Fig. 2). GPT-3 has the lowest performance for humor-based and arbitrary
target group label dogwhistles, and the highest performance for
representatives (Bogeymen), phonetic-based target group labels, and policies
(Table A.4).

Persona | Proportion | 95% CI
---|---|---
homophobic | 0.737 | 0.069
Islamophobic | 0.654 | 0.060
climate change denier | 0.595 | 0.106
anti-Asian | 0.583 | 0.126
conservative | 0.563 | 0.100
anti-Latino | 0.560 | 0.107
racist | 0.532 | 0.029
anti-vax | 0.490 | 0.101
antisemitic | 0.462 | 0.029
white supremacist | 0.448 | 0.029
anti-liberal | 0.292 | 0.106
transphobic | 0.229 | 0.024

Table A.3: Proportion of dogwhistles with covert meanings identified by GPT-3
across personae with at least 50 GPT-3 generations. GPT-3 has the lowest
recognition of transphobic dogwhistles and the highest recognition of
homophobic and Islamophobic dogwhistles.

Dogwhistle Type | Proportion | 95% CI
---|---|---
concept (humor) | 0.244 | 0.063
arbitrary group label | 0.261 | 0.046
stereotype-based descriptor | 0.311 | 0.060
persona signal (symbol) | 0.331 | 0.032
persona signal (self-referential) | 0.444 | 0.046
persona signal (shared culture) | 0.448 | 0.054
concept (values) | 0.475 | 0.026
stereotype-based group label | 0.497 | 0.031
concept (policy) | 0.519 | 0.036
phonetic-based group label | 0.533 | 0.127
representative (Bogeyman) | 0.618 | 0.063

Table A.4: Proportion of dogwhistles with covert meanings identified by GPT-3
by the dogwhistle type.

Glossary Example | GPT-4 Response | Term | Meaning
---|---|---|---
| The cosmopolitan elite look down on the common
---
affections that once bound this nation together: things
like place and national feeling and religious faith. The
cosmopolitan agenda has driven both Left and Right.
It’s time we ended the cosmopolitan experiment and
recovered the promise of the republic.
| The dogwhistle in this message is the term "cosmopolitan elite,"
---
which is often used to target a specific group of people, such as
urban, educated, and often liberal individuals, without explicitly
naming them. It can also be interpreted as an anti-Semitic or
anti-immigrant message, as "cosmopolitan" has historically been
used to criticize Jewish people and immigrants for their perceived
lack of loyalty to their country.
✔ | ✔
| We have got this tailspin of culture, in our inner cities in
---
particular, of men not working and just generations of men
not even thinking about working or learning the value and
the culture or work.
| The dogwhistle in this message is the reference to "inner cities"
---
and "generations of men not working," which can be interpreted
as coded language targeting African Americans or other minority
groups living in urban areas, perpetuating stereotypes about
laziness and unemployment.
✔ | ✔
|  #IStandWithJKRowling because she wrote a series of
---
books that made readers out of millions of children and
adolescents. And she has every right to express her opinion
on cultural matters. And because she’s right about them.
| The dogwhistle in this message is "she’s right about them," which
---
indirectly supports J.K. Rowling’s controversial opinions on gender
and transgender issues without explicitly mentioning them.
✘ | ✔
And together, we will win  |  | The dogwhistle in this message is the use of the colors  ,
---
which are associated with the women’s suffrage movement. This
suggests that the message is aimed at garnering support from
women or feminists without explicitly stating it.
✔ | ✘
Did you see Kyle? He’s about this tall.. |  | The dogwhistle in this message is "Did you see Kyle? He’s about
---
this tall.." which could be a reference to the white supremacist
symbol of the "K" hand sign, where the fingers form the letters
"W" and "P" for "white power." The name "Kyle" is sometimes
associated with white supremacist groups.
✘ | ✘

Table A.5: Manual proof-of-concept analysis for using GPT-4 to identify
dogwhistles in-context. The columns on the right indicate whether GPT-4
correctly identifies the dogwhistle term and its covert meaning, respectively.
Figure A.3: Proportion of GPT-3 generations that correctly identify
dogwhistles’ covert meanings across prompted dogwhistle definitions and secret
cues.

Definition Source | Mean | 95% CI
---|---|---
None Provided | 0.191 | 0.025
Merriam-Webster | 0.438 | 0.031
Albertson (2015) | 0.449 | 0.031
Bhat and Klein (2020) | 0.513 | 0.032
Henderson and McCready (2018) | 0.515 | 0.032
Wikipedia | 0.534 | 0.032

Table A.6: Proportion of GPT-3 generations that correctly identify
dogwhistles’ covert meanings for each dogwhistle definition provided in
prompting.

#### Variation across dogwhistle definitions

Only 19.1% of GPT-3 generations include the correct covert meaning when
prompted with no dogwhistle definition. Prompting GPT-3 with any of the five
dogwhistle definitions greatly improved performance over no definition
provided, but the extent varied, with the Merriam-Webster definition yielding
the lowest improvement (43.8%) and Wikipedia yielding the highest (54.3%)
(Table A.6). The boost in performance by adding the secret cue depends on the
specific definition used; the secret cue has a bigger effect when using the
Merriam-Webster and Albertson (2015) definitions (Figure A.3).

Figure A.4: Toxicity, severe toxicity, and identity-attacking scores from
Perspective API preliminary experiment. When slurs or standard group labels
are substituted with dogwhistles, sentences are rated as significantly less
toxic.

#### Where does GPT-3 perform poorly?

Most unrecognized dogwhistles are part of the informal register, especially
symbols (e.g. the transphobic spiderweb or cherry emojis). Other unrecognized
dogwhistles include “Operation Google” terms (e.g. Skype, Yahoo), more recent
terms (e.g. Let’s Go Brandon), and several antisemitic and transphobic
dogwhistles whose covert meanings are especially context-dependent (e.g. adult
human female, XX, (Wikipedia) early life, fellow white people). Unrecognized
formal dogwhistles tend to be extremely subtle and nuanced (e.g. Dred Scott as
a conservative anti-abortion dogwhistle) or are highly-conventionalized
phrases that may be far more commonly used without the covert implicature
(e.g. the antisemitic dogwhistle poisoning the well).

#### Where does GPT-3 perform well?

GPT-3 readily identifies Islamophobic dogwhistles (e.g. radical Islam, Barack
Hussein Obama), many antisemitic conspiracy theories (e.g. Khazars, Kalergi
Plan, Israel Lobby), some racist dogwhistles whose covert meanings are more
widely discussed among the out-group (e.g. inner-city, ghetto, thug, war on
drugs, black-on-black crime), some alt-right memes (e.g. Pepe the Frog), and
conservative political rhetoric (e.g. balance the budget, judicial activism,
job creators).

###  A.3 Details for toxicity detection

Table A.7 shows a sample of the 237 hateful sentence templates used from
HateCheck (Röttger et al., 2021). We used the subset of HateCheck templates
labeled as hateful and containing a placeholder for an identity term in either
adjectival, singular nominal, or plural nominal form. We filled in these
placeholders with either a standard group label, dogwhistle, or slur for three
personae: antisemitic, racist, and transphobic. Specific terms used can be
found in Table A.8.

For all personae and toxicity measures provided by Perspective API,
dogwhistles are rated as less toxic than slurs and standard group labels
(Figure A.4. Interestingly, except for the N-word, Perspective seems to not
recognize the extreme offensiveness of most slurs, and thus toxicity scores
for hateful sentences containing slurs are similar to or lower than scores for
the same hateful sentences containing standard group labels.

Template
---
So sick of being around [IDENTITY_P] like you.
[IDENTITY_P] are disgusting.
You [IDENTITY_P] are all dishonest.
[IDENTITY_P] are human garbage.
Your [IDENTITY_A] lives are worthless.
Never trust a single word said by a [IDENTITY_S].
I will cut their [IDENTITY_A] throats.
I would love to see those [IDENTITY_P] suffer.
All [IDENTITY_P] should fuck off forever.
Of course that dumbass was a [IDENTITY_S].
[IDENTITY_P] think they’re special. I hate them.
Isn’t it typical of a [IDENTITY_S] to be that stupid?

Table A.7: Sample of template hateful sentences from HateCheck (n=237). We
create inputs for Perspective API by replacing identity term placeholders with
a standard group label, dogwhistle, or slur in either adjectival (IDENTITY_A),
singular nominal (IDENTITY_S), or plural nominal form (IDENTITY_P).

Racist | Antisemitic | Transphobic
---|---|---
African-American | Jewish | transgender
Black | Jew | trans
inner-city | cosmopolitan | AGP
welfare queen | cosmopolitan elite | autogynephile
thug | coastal elite | AAP
dindu | globalist | autoandrophile
gangbanger | international banker | clownfish
ghetto | cultural Marxist | troon
urban | Hollywood elite | TIF
superpredator | Khazar | TIM
n****r | k**e | t****y
c**n | h**b | s*****e

Table A.8: Racist, antisemitic, and transphobic terms used for toxicity
analysis. We substitute identity placeholders in HateCheck templates (Röttger
et al., 2021) with these terms to create inputs to Perspective API.