SlowGuess/NAACL_2022_OCR huggingface dataset:
Given an initial set of posts from our categories, we then randomly sample up to 600 posts, stratifying by toxicity label, vulgarity, AAE, and anti-Black meaning. Our final sample contains 571 posts, as outlined in Table 2 and Figure 5.
D.2 Breadth-of-Posts Survey details
As in the breadth-of-workers study, we recruit participants using a pre-qualifying survey on MTurk. Then, we set up a second MTurk task to collect toxicity ratings, and annotator attitudes and identities. For each post, we collected two ratings from white conservative workers, two from white liberal workers, and two from Black workers. To bet
Figure 4: Average ratings of offensiveness and racism for each tweet category in the breadth-of-workers controlled study. All differences are significant  $(p < 0.001)$  after correcting for multiple comparisons.
ter mirror the crowdsourcing setting and to reduce the annotator burden, we shorten the task to only ask one question per attitude (listed in  $\S \mathrm{A}$  ). We also asked one attention check question to ensure data quality.
For this study, our final dataset contains 3,171 ratings from  $N = 173$  participants. Our participants were  $53\%$  were men,  $45\%$  women, and  $< 2\%$  non-binary, identified as  $76\%$  white,  $20\%$  Black, and  $< 4\%$  some other race, and spanned the political spectrum from  $54\%$  liberal to  $30\%$  conservative, with  $16\%$  centrists or moderates.
D.3 Selecting Attitude Questions
In order to simplify the annotation task for annotators, we abridged the attitude scales to only one item. Using the data from the breadth-of-workers study, we select the question that best correlated with all toxicity ratings. Specifically, for each scale, we first take the tweet category with the highest correlation with toxicity (e.g., anti-Black posts for RACISTBELIEFS), and then take the item whose response scores correlated most with the toxicity rating for those posts. Those items are bolded in  $\S A$ .
E Further Breadth-of-Workers Results
We show all associations between attitudes and toxicity ratings in Table 10.
Additionally, we investigate the differences in the overall toxicity ratings of anti-Black vs. AAE
<sup>16</sup>As before, we discard 255 ratings where workers failed an attention check.
Rated as Offensive	Rated as Racist
Anti-Black	AAE	Vulgar (OnI)	Anti-Black	AAE	Vulgar (OnI)
EMPATHY	r = 0.285**	n.s.	n.s.	n.s.	r = 0.286**	n.s.	n.s.	n.s.
ALTRUISM	r = 0.380**	n.s.	n.s.	n.s.	r = 0.441**	n.s.	n.s.	n.s.
HARMOfHATESpeech	r = 0.451**	n.s.	n.s.	n.s.	r = 0.528**	n.s.	n.s.	n.s.
FREEOFFSpeech	r = -0.394**	n.s.	n.s.	n.s.	r = -0.467**	n.s.	n.s.	n.s.
RACISTBELIEFS	r = -0.513**	n.s.	n.s.	n.s.	r = -0.574**	r = 0.089*	n.s.	n.s.
LINGPURISM	r = -0.154**	n.s.	r = 0.106*	n.s.	r = -0.167**	n.s.	n.s.	n.s.
TRADITIONALISM	r = -0.206**	n.s.	r = 0.252**	n.s.	r = -0.237**	n.s.	n.s.	n.s.
Politics (lib.: 0, cons.: 1)	r = -0.374**	n.s.	r = 0.171**	n.s.	r = -0.441**	r = 0.076†	n.s.
Gender (men: 0, women: 1)	d = 0.321**	n.s.	n.s.	n.s.	d = 0.341**	n.s.	n.s.
Race (White: 0, Black: 1)	d = 0.301*	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
Table 10: Full set of results from our analyses of the breadth-of-workers study of 15 posts, presented as Pearson  $r$  or Cohen's  $d$  effect sizes, along with significance levels  $\left( {{}^{ \dagger  } : p < {0.075}, *  : p < {0.05}, *  *  : p < {0.001}}\right)$  . We correct for multiple comparison for variable relationships that were exploratory (i.e., not discussed as hypotheses in §4-6).
vs. vulgar posts? (Figure 4). Overall, anti-Black tweets were rated as substantially more offensive and racist than AAE or vulgar tweets (with effect sizes ranging from  $d = 2.4$  to  $d = 3.6$ ). Additionally, vulgar tweets were rated as more offensive than AAE tweets ( $d = -0.29$ ,  $p < 0.001$ ).
Surprisingly, we also found that AAE tweets were considered slightly more racist than vulgar tweets  $(d = 0.19$ ,  $p < 0.001$ ). To further inspect this phenomenon, we performed exploratory analyses by computing the differences in ratings of racism for AAE and vulgar broken down by annotator gender, race, and political leaning. We found that AAE tweets were rated as significantly more racist than vulgar tweets only by annotators who were white or liberal  $(d = 0.20$  and  $d = 0.22$ , respectively, with  $p < 0.001$  corrected for multiple comparisons), compared to Black or conservative. There were no significant differences when looking at men and women separately.
F Further Breadth-of-Posts Results
To account for the varying number of posts that each annotators could rate, we use a linear mixed effects model  $^{17}$  to compute associations between each post's toxicity ratings and identities or attitudes. Specifically, we our linear model regresses the attitude score onto the toxicity score, with a random effect for each worker. $^{18}$
See Figure 5 and Table 11.
Figure 5: Venn diagram of number of tweets in each of the categories.
G PERSPECTIVEAPI Case Study: Details & Results
G.1 Details
We first obtain PERSPECTIVE toxicity scores for all the posts in our breadth-of-posts study (§3.2).<sup>19</sup> Then, we split workers into two different groups for each of our attitudes and identity dimensions. For attitudes and political leaning, we assign each annotator to a "high" or "low" group based on whether they scored higher or lower than the mean score on that attitude scale. For gender and race, we use binary bins for man/woman and white/black.
Then, for each attitude or identity dimension, we compute the Pearson  $r$  correlation between the PERSPECTIVE score and the toxicity ratings from the high and low groups, considering posts from potentially overlapping categories (e.g., AAE and potentially vulgar posts). Finally, we compare the high and low correlations using Fisher's  $r$ -to-  $z$  transformation (Silver and Dunlap, 1987).
Using the Python statsmodels implementation.
18In R-like notation, toxicity~attitude+ (1|WorkerId).19the API was accessed in October 2021
Rated as Offensive	Rated as Racist
Anti-Black	AAE	Vulgar OI	Vulgar OnI	Anti-Black	AAE	Vulgar OI	Vulgar OnI
EMPATHY	n.s.	n.s.	0.168†	n.s.	n.s.	n.s.	n.s.	n.s.
ALTRUISM	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
HARMOfHATESpeechCH	0.117†	n.s.	0.169†	n.s.	0.154*	n.s.	n.s.	n.s.
FREEOFFSpeechCH	n.s.	n.s.	n.s.	n.s.	-0.138†	0.217*	n.s.	n.s.
RACISTBELIEFS	-0.131*	0.133†	n.s.	n.s.	-0.185*	n.s.	n.s.	n.s.
LINGPURISM	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
TRADITIONALISM	n.s.	0.137*	0.152*	n.s.	n.s.	0.110†	n.s.	n.s.
Politics (lib: 0, cons.: 1)	n.s.	0.143*	n.s.	0.134†	n.s.	0.206**	0.172*	0.196*
Gender (men: 0, women: 1)	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
Race (White: 0, Black: 1)	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
Table 11: Associations between the annotator demographic and attitude variables and their ratings of offensiveness and racism on the posts from the breadth-of-posts study. We break down the results by category, but categories are overlapping. Only significant associations ( $\beta$  coefficients from a mixed effects model) are shown ( $\dagger$ :  $p < 0.075$ ,  $*$ :  $p < 0.05$ ,  $*\*$ :  $p < 0.001$ ; Holm-corrected for multiple comparisons).
See Table 12 and Figures 6-13.
Rated as Offensive	Rated as Racist
Anti-Black	AAE	Vulgar OI	Vulgar OnI	Anti-Black	AAE	Vulgar OI	Vulgar OnI
EMPATHY	n.s.	n.s.	-0.10*low	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
ALTRUISM	n.s.	-0.08*low	-0.11*low	n.s.	n.s.	-0.10*low	n.s.	n.s.	n.s.	n.s.
HARMOFHATESpeech	n.s.	n.s.	-0.10†low	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
FREEOFFSpeech	n.s.	n.s.	0.18*high	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
RACISTBELIEFS	n.s.	n.s.	0.15*high	n.s.	0.12†high	n.s.	n.s.	n.s.	n.s.	n.s.
LINGPURISM	n.s.	0.09*high	0.11*high	n.s.	n.s.	0.14*high	n.s.	0.08†high	n.s.	0.09†high
TRADITIONALISM	n.s.	n.s.	n.s.	n.s.	n.s.	0.10*high	n.s.	n.s.	n.s.	n.s.
Politics (lib.: 0, cons.: 1)	n.s.	n.s.	-0.09†lib.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.	n.s.
Gender (men: 0, women: 1)	n.s.	0.10*women.	-0.10*men	n.s.	n.s.	0.22*women.	n.s.	n.s.	n.s.	n.s.
Race (White: 0, Black: 1)	n.s.	-0.07†white	n.s.	n.s.	n.s.	-0.08†white	n.s.	n.s.	n.s.	n.s.
Table 12: We correlated the PERSPECTIVE API toxicity scores with offensiveness/racism ratings by our annotators, breaking them into two bins based on their attitude scores. Then, we used Fisher's z-to-r test to measure whether the differences in correlations between the annotators who are high/low were significant ( $\dagger$ :  $p < 0.1$ ,  $*$ :  $p < 0.05$ ).
Figure 6: PERSPECTIVE API and ratings of offensiveness of anti-Black tweets.
Figure 7: PERSPECTIVEAPI and ratings of racist of anti-Black tweets.
Figure 8: PERSPECTIVE API and ratings of offensiveness of AAE tweets.
Figure 9: PERSPECTIVEAPI and ratings of racist of AAE tweets.
Figure 10: PERSPECTIVEAPI and ratings of offensiveness of vulgar-OI tweets.
Figure 11: PERSPECTIVEAPI and ratings of racist of vulgar-OI tweets.
Figure 12: PERSPECTIVEAPI and ratings of offensiveness of vulgar-ONI tweets.
Figure 13: PERSPECTIVE API and ratings of racist of vulgar-ONI tweets.