Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs
Authors: Portia Cooper
Mihai Surdeanu
Eduardo Blanco
Venue: EMNLP 2023 Findings (2023)
Keywords: Homoglyphs
Dataset
Hate Speech Detection
Twitter

Abstract: To avoid detection by current NLP monitoring applications, progenitors of hate speech often replace one or more letters in offensive words with homoglyphs, visually similar Unicode characters. Harvesting real-world hate speech containing homoglyphs is challenging due to the vast replacement possibilities. We developed a character substitution scraping method and assembled the Offensive Tweets with Homoglyphs (OTH) Dataset (N=90,788) with more than 1.5 million occurrences of 1,281 non-Latin characters (emojis excluded). In an annotated sample (n=700), 40.14% of the tweets were found to contain hate speech. We assessed the performance of seven transformer-based hate speech detection models and found that they performed poorly in a zero-shot setting (F1 scores between 0.04 and 0.52) but normalizing the data dramatically improved detection (F1 scores between 0.59 and 0.71). Training the models using the annotated data further boosted performance (highest micro-averaged F1 score=0.88, using five-fold cross validation). This study indicates that a dataset containing homoglyphs known and unknown to the scraping script can be collected, and that neural models can be trained to recognize camouflaged real-world hate speech.

Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs
Portia Cooper, Mihai Surdeanu, Eduardo Blanco University of Arizona, Tucson, AZ, USA
portiacoppefeariznn. edu
Abstract
2021). For instance, the Latin "a" (U+0061) and the Cyrillic "a" (U+0430) are nearly indistinguish- able to the human eyc, yet they belong to different Unicode character families. Currently, there are al- most 150,000 Unicode characters," which presents extensive substitution possibilities.
To avoid detection by current NLP monitoring applications, progenitors of hate speech often replace one more letters in offensive words with homoglyphs, visually similar Unicode characters. Harvesting real-world hate speech containing homoglyphs is challenging due to the vast replacement possibilities. We devel- oped character substitution scraping method and assembled the Offensive Tweets with Ho- moglyphs (OTH) Dataset! (N=90,788) with more than 1.5 million occurrences of 1,281 non- Latin characters (emojis excluded). In an an- notated sample (n=700), 40.14% of the tweets were found to contain hate speech. We assessed the performance seven transformer- based hate speech detection models and found that they performed poorly in a zero-shot setting (F1 scores between 0.04 and 0.52), but nor- malizing the data dramatically improved detec- tion (FI scores between 0.59 and 0.71). Train- ing the models using the annotated data further boosted performance (highest micro-ave averaged score=0.88, using five-fold cross validation). This study indicates that dataset containing homoglyphs known and unknown to the scrap- ing script can be collected, that neural mod- els can be trained to recognize camouflaged real-world hate speech.
Despite the prevalence of online hate speech con- taining homoglyphs, the collection of organically generated data of this type is rare. To remedy this scarcity, we developed a dataset of real-world of- fensive text containing homoglyphs and used this data to train existing hate speech detection models. In particular, our contributions are:
Developing a novel character substitution scraping method used to assemble a dataset of 90,788 tweets with offensive words con- taining homoglyphs. To our knowledge, our dataset is the first to be composed of real- world, homoglyph-lader texts.
Evaluating the effectiveness of replacing Latin characters with homoglyphs as an obfuscation strategy by testing the zero-shot performance of seven open-source transformer- based hate speech detection models hosted by Hugging Face on three versions of an annotated sample of the dataset: (1) original with homoglyphs, (2) partially normalized (Cyrillic homoglyphs replaced with the corresponding Latin letters), and (3) fully normalized (all non-Latin homo- glyphs replaced with the corresponding Latin characters).
1 Introduction
Disclaimer:
This paper includes language that some readers might find offensive.
Hate speech, discriminatory language against individuals or groups based on race/ethnicity, na- tionality, gender, religion, LGBTQ+ identity, or disability status, is banned by Facebook, YouTube, and other major platforms. A common strategy to mask hate speech is replacing offensive words with homoglyphs, Unicode charac- that are visually homogeneous (Boucher et al.,
Demonstrating that models can be trained to recognize real-world hate speech containing homoglyphs known and unknown to the scrap- ing script.
more letters in
'https: http://ggttuuub.cocoooperrcreeee com/pcaoperrcader/ offensive- Lefensive-Tweets Tweets-wil with oiw.iitttoogggyyy---o--tttaaaaaa
http://wwwwmiiceeeeerr/rrr/ssass Charbyyear.html

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2 Related Work
Latin
Cyrillic
Letter
Unicode Value
Homoglyph(s)
Unicode Value(s)
Previous work that explored human-generated text with homoglyphs includes the ANTHRO algorithm, which evaluated the phonetic properties of words locate character-based perturbations, including homoglyphs (Le et al., 2022). Boucher et (2021) used homoglyphs, letter reordering, and letter dele- tion to test the detection capabilities of NLP mod- cls. Additionally, Kurita et al. (2019) artificially produced offensive text containing homoglyphs to simulate adversarial attacks.
U40061
U40430
U+0062
B, b
U+0432, U+044C
U40063
U+0441
U+00644
U+0501
U+00655
U+0435
U+00666
U-40067
G
U-050D
U+0068
D,
U+043D, U+04BB
U-40069
I
U-04566, U+04CF
U+0066
U+0458
U+006B
U-043A
U+006C
U+006D
U-043C
Woodbridge et al. (2018) investigated obfusca- tion mitigation using a Siamese convolutional neu- ral network to convert homoglyphs into the char- acters they resembled. Similarly, Ginsberg and Yu (2018) proposed "visual scanning" method to detect and predict homoglyphs. Other work has inventoried homoglyphs (Suzuki et al., 2019) and identified previously unknown homoglyphs (Deng al., 2020).
U+006E
U+043F
U+006F
U+043E
U+0070
U+0440
U+0071
U+05 IB
U+0072
U+0433
U+00733
U+0455
U+0074
U+0442
U'00755
U+0446
U40076
U+0475
U+0077
W;
C+05ID.
U+0461
U40078
U+0445
U+0079
Y.
U+MAF,, U=0443
U4007A
Finally, several studies have evaluated phishing attacks in which homoglyphs were used to imitate corporate domain names to deceive users and ex- tract personal information (Maneriker et al., 2021; Lee et al., 2020; Wolff and Wolff, 2020). These studies involved training models using artificially created data containing homoglyphs and did not evaluate real-world content.
Table 1: Unicode values of Latin letters and correspond- ing Cyrillic homoglyphs.
Latin letters "f", "I", and "z" had no Cyrillic ho- moglyphs thus yielded no variations. For example, the word "fuck" yielded only three query terms (fuck, fuck, and fuck). This process produced 247 query terms that included 26 of the Cyrillic ho- moglyphs for Latin letters, as "v" and "x" were not present in the 41 offensive words used to generate the query terms.
3 Approach
3.1 Scraping Twitter
Tweets were collected from Twitter (now renamed X) in December 2022 using the Python Tweepy Library (computational cost detailed in Appendix A.I). Scripting was used to create query terms derived from the 41 most offensive American En- glish words ranked by native English-speaking col- lege students at large U.S. metropolitan university (Bergen, 2016) (list in Appendix A.2). Query terms were generated by replacing each Latin letter in the offensive words with the corresponding Cyrillic homoglyph(s) from the Unicode Confusable Stan- d (Table 1). The Latin letters "b". "h", "i", "w". and "y" had multiple Cyrillic homoglyphs, thus words containing these letters generated more than one query term for each letter. For example, the word "bitch" yielded eight variations (Ritch, bitch, bitch, bltch, birch, bitch, bitcH, and bitch). The
A total of 93,042 tweets were collected, includ- ing 2,254 duplicates created by the presence of multiple query terms in a single tweet. Duplicate tweets were removed, resulting in 90,788 tweets, which we named the Offensive Tweet with Homo- glyphs (OTH) Dataset. Figure provides an ex- ample of a homoglyph-laden tweet included in the OTH Dataset. The metadata for each tweet was col- lected from Twitter for aggregate analysis purposes. We also calculated the number of unique non-Latin characters (emojis excluded) present in the dataset and the number times each was detected.
3.2 Annotation
From the OTH Dataset, random sample of 700 tweets was selected. Using a detailed codebook (Appendix A.3), two human annotators indepen- dently evaluated the tweets in the sample (annotator
hht //wwww unicode or wttp://bblic//ccrrtt/ revision-83.1

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OTH
Annotated
Characters
Dataset
Sample n=700
rumfurs? impresalive eltch, of lusst wothr spreuding sumethhing clse moaldes lege
N=90,788
Cyrillic homoglyphs for Latin letters
10:44 PM Oct 2011
Unique characters detected
27
21 9,759
Occumences
1,264,406
Additional Cyrillic characters
Unique characters detected
72 125,973
Homoglyph-laden tweet in the Offensive Tweets with Homoglyphs (OTH) Dataset.
Figure I:
Occumences
1,129
Other non-Latin characters" Unique characters detected
1,182 136,346
93 1,164
Occumencess
information in Appendix A,4), and an IRB exemp- tion for research not involving human participants was granted.
Total
Unique characters detected Occurrecces
1,281
149
1,526,725
12,052
"Emojis excluded
Intercoder agreement exceeded 96.00% for all codes, and Cohen's is Kappa ranged from 0.80 to 1.00 (Appendix A.5, Table 5). Coding disagreements were discussed and reconciled.
Table 2: Character composition of the Offensive Tweets with Homoglyphs (OTH) Dataset and the annotated sample.
3.3 Tweet Normalization
detailed in Appendix A.7). The micro-averages of accuracy, precision, recall, and Fi-score across the five folds were calculated.
A survey of existing homoglyph normalization re- sources was performed, and widely used Python libraries were evaluated (Appendix A. No tool was found which automated the conversion of all possible homoglyphs into the Latin characters they resemble. Normalization in the present study was accomplished using scripting and manual com- pilation. Two normalized versions of the anno- tated sample were created: (1) partially normal- ized (Cyrillic homoglyphs replaced with the corre- sponding Latin letters using custom scripting) and (2) fully normalized (all non-Latin homoglyphs replaced with the corresponding Latin characters using manual compilation).
4
Results
4.1
OTH Dataset
The 90,788 tweets in the OTH Dataset posted by 31,878 unique author IDs. The dataset included
1,264,406 occurrences of 27 Cyrillic homoglyphs for Latin letters [only the Cyrillic "q" (U+05 IB) was not detected] (Table 2). Importantly, 72 addi- tional Cyrillic characters occurred 125,973 times, and 1,182 other non-Latin characters (emojis ex- cluded) occurred 136,346 times. The dataset in- cluded an average of 16.82 Cyrillic and other non- Latin characters per tweet.
3.4 Base Models
The original, partially normalized, and fully nor- malized versions of the annotated sample were separately through seven open-source transformer- based hate speech detection models hosted by Hug- ging Face (computational cost detailed in Appendix A.7). All selected models were trained (at least in part) on hate speech collected from social me- dia platforms (a summary of each of the utilized models is included in Appendix A.8). Accuracy, precision, recall, and F1 F1-score were calculated.
As shown in Figure 2, the homoglyphs with the highest number of occurrences were the Cyrillic "e" (n=295,039, U+0435), "o" (n=238.643) U+043E), and "a" (n=225,668 U+0430). The non-Cyrillic ho- moglyphs with the highest number of occurrences were "f" (n=26,246, U+00ED), "a" (n=24,730, U+03C3), and "a" (n=23,465, U+03B1).
Of the 247 query terms searched, 156 returned tweets. The earliest tweet in the OTH Dataset was posted on February 19, 2009 (31 months after the inception of Twitter), and the most recent tweet was posted December 20, 2022. The tweets were assigned 43 language identifiers by Twitter, and the majority were classified as Czech (62.04%). Only 24.27% were classified as English by Twitter (Appendix A.9, Table 6). even though all tweets
3.5 Five-Fold Cross Validation
Finally, five-fold cross validation was performed on each of the seven models using the original version of the annotated sample to evaluate the extent to which the models learned when exposed to real- world data with homoglyphs (computational cost

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Original Version
Partially Normalized Version
Fully Normalized Version
Model
F1
A
P
R
F1
A
1
0.60
0.67
0.02
0.04
0.71
0.79
0.38
0,52
0.74
0,81
0,48
0.60
2
0.60
0.49
0.16
0.24
0.65
0.55
0.65
0.60
0.69
0.58
0.78
0.67
0.65
0.83
0.17
0.29
0.75
0.79
0.52
0.63
0.78
0.77
0.63
0.70
4 0.56
0.46
0.46
0.46
0.50
0.44
0.91
0.60
0.53
0.46
0.98
0.62
5 0.58
0.47
0.40
0.43
0.60
0.50
0.73
0.59
0.63
0.54
0.66
0.59
6 0.57
0.47
0.57
0.52
0.66
0.55
0.84
0.67
0.70
0.59
0.83
0.69
7
0.63
0.54
0.44
0.49
0.66
0.55
0.80
0.65
0.72
0.61
0.84
Table 3: Accuracy (A), precision (P), recall (R), and FI-scores (FI) for seven hate speech detection models on three versions of the annotated sample (n=700) of the Offensive Tweets with Homoglyphs (OTH) Dataset: (1) original with homoglyphs, (2) partially normalized (Cyrillic homoglyphs replaced with the corresponding Latin letters), and (3) fully normalized (all non-Latin homoglyphs replaced with the corresponding Latin characters).
B
4.3 Zero-Shot Model Performance
3
n
6
CK
eer
K
M
On the original version of the annotated sample with homoglyphs, the Fi-scores of the seven hate speech detection models ranged from 0.04 to 0.52 (Table 3). On the partially normalized version of the annotated sample (Cyrillic homoglyphs re- placed with the corresponding Latin letters), FI- scores ranged from 0.52 to 0.67. On the fully nor- malized version of the annotated sample (all non- Latin characters replaced with the corresponding Latin characters), Fl Fi-scores ranged from 0.59 to 0.71.
X
4.
bl u
bi
p
T
a
nn
R
HH
1
EAT
S
N
Figure 2: Word cloud representation of 99 Cyrillie char-
acters present in the Offensive Tweets with Homoglyphs (OTH) Dataset by occurence (3=3,39,,377).
4.4 Five-Fold Cross Validation
in the OTH Dataset included at least one English- language query term.
In the five-fold cross validation on the annotated sample with homoglyphs, Fi-scores ranged from 0.41 0.88 (Table 4).
4.2 Annotated Sample
Model
P
R
F1
0.90
0.88
0.86
0.87
The annotated sample resembled the OTH Dataset in terms of character composition and included an average of 17.22 Cyrillic and other non-Latin characters per tweet.
2
0.69
0.84
0.27
0.41
0.81
0.77
0.76
0.77
0,91
0.91
0.86
0.88
0.90
0.88
0.86
0.87
0.89
0.87
0.85
0.86
In the annotated sample, 40.14% of tweets were classified hate speech by human annotators. Most these tweets (87.54%) included misogy- nistic hate speech. Tweets referencing hate speech related to LGBTQ+ identity (5.34%), race/ethnicity (3.91%), disability status (1 (1.42%), and religion (1.07%) were less common. No hate speech related to nationality ally, 97.71% % of tweets were classified offensive; 20.43% were labeled sexually explicit; and 5.14% referenced violence or aggressive acts. The body text of the vast majority of tweets (92.43%) was classified as English by the annotators.
0.89
0.85
0.88
0.87
Table 4: Accuracy (A), precision (P), recall (R), and F1- scores (FI) across five-fold cross validation for seven hate speech detection models on the annotated sam- ple (n=700) of the Offensive Tweets with Homoglyphs (OTH) Dataset.
found in the sample. Addition-
Discussion
Our character substitution scraping method yielded
90,788 tweets containing more than 1.5 million occurrences of 1,281 non-Latin characters (emojis

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excluded). The search strategy used Cyrillic ho- moglyphs for Latin letters to assemble broader collection of non-Latin characters. As expected, the bulk of the OTH Dataset (82.82%) was com- prised Cyrillic homoglyphs. However, 262,319 occurrences of 1,254 other non-Latin characters were also captured, including 72 additional Cyrillic characters and 1,182 characters from other Uni- code character families. The most common Cyril- lie characters were "a" (U+0430), "e" (U+0435), and "o" (U+043E), which are homoglyphs for the Latin letters "a", "e", and "o." The most common non-Cyrillic characters were "a" (U+03B1), "f" (U+00ED), and "o" (U+03C3), which are homo- glyphs for the Latin letters "a", "i", and "o". These results may reflect malicious users' preference for homoglyphs that mimic Latin vowels.
to only 21.00% of tweets, but human annotators found that 92.43% of the tweets were written in English. Twitter classified the majority of tweets in the annotated sample (60.50%) and the OTH Dataset (62.04%) as Czech. These results are espe- cially interesting because the Czech language does not include Cyrillic characters, which were used to generate the query terms.
6 Conclusion
Infusing hate speech with homoglyphs was found to be an effective strategy to avoid detection. In the present study, existing hate speech detection models were ineffective against obfuscation of this type. The OTH Dataset offers training data to build hate speech detection models inoculated against real-world homoglyphs.
The noise produced by the homoglyphs impeded the performance of all seven hate speech detec- tion models tested. Fully normalizing the data by replacing all non-Latin characters with the corre- sponding Latin characters dramatically improved model performance. Most notably, Model I's F1- score jumped from 0.04 to 0.60.
Limitations
Cyrillic homoglyphs for Latin letters were used to generate the query terms in the present study because they are widely used and were exempt from the normalization that Twitter performs on query terms prior to searching its repository. A more diverse dataset may be achieved by expand- ing the search strategy to include (1) homoglyphs from multiple Unicode character families, (2) com- plex homoglyph substitution combinations in query terms, and (3) a broader list of offensive words to generate query terms.
In the five-fold cross validation, five models achieved F1-scores that exceeded 0.85. Conversely, Model 2 performed poorly (F1-score 0.41). This may be related to the model's original training data, which included only hate speech labeled as LGBTQ+ identity and nationality-caaegoriie of hate speech which were rare within the annotated sample in the present study. The performance of the other six models demonstrates that neural clas- sifiers can be trained to recognize real-world hate speech masked by homoglyphs. This is exciting result, considering that the dataset contains many homoglyphs unknown the scraping script, and thus these perturbations could not be addressed through deterministic normalization.
Ethical Consideration
Due to the offensive nature of the OTH Dataset and the data sharing policy of X (Twitter), only the Tweet IDs of the OTH Dataset will be published.
References
The annotated sample analyzed in the present study included a 40%-60% split between tweets with and without hate speech. The large volume of hate speech tweets with misogynistic content (87.54%) found in the annotated sample is notable. This result is consistent with prior survey of Twit- ter that found 419,000 female slurs were posted on average each day (Felmlee al., 2020).
Sai Saket Aluru, Binny Mathew, Punyajoy Saha, and Animesh Mukherjee. 2020. Deep learning models for multilingual hate speech detection. arXiv preprint 2rXiv:0044.06-633
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa-Anke, and Leonardo Neves. 2020. TweetE- val:Unified Benchmark and Comparative Evaluation for Tweet Classification. In Proceedings of Findings of EMNLP.
Homoglyphs also appeared interfere with Twitter's internal system that classifies the body text language of tweets. In the annotated sample, Twitter assigned the English language identifier
Benjamin K. Bergen. 2016. What the F: What Swear- ing Reveals About Our Language. Our Brains, and Ourselves. Basic Books, New York, NY.

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Nicholas P. Boucher, Ilia Shumailov, Ross Anderson, and Nicolas Papernot. 2021. Bad characters: Im- perceptible nlp attacks. 2022 IEFE Symposium on Security and Privacy (SP), pages 1987-2004.
Max Wolff and Stuart Wolff. 2020, Attacking neural text detectors. ArXiv, abs/2002.11768.
Jonathan Woodbridge, Hyrum S. Anderson, Anjum Ahuja, and Daniel Grant. 2018. Detecting homo- glyph attacks with a siamese neural network. In 2018 IEFE Security and Privacy Workshops (SPW), pages 22-28.
Perry Deng. Cooper Linsky, and Matthew Wright. 2020. Weaponizing unicodes with deep learning identifying homoglyphs with weakly labeled data. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI).
A
Appendix
Diane Felmlee, Paulina Inara Rodis, and Amy Zhang- 2020. Sexist slurs: Reinforcing feminine stereotypes online. Sex Roles, 83.
A.1
Computational Costs of Scraping Twitter
An estimated 48 total hours were necessary compile the OTH Dataset from the Twitter corpus. Tweet scraping was performed locally.
Avi Ginsherg and Cui Yu. 2018. Rapid homoglyph pre- diction and detection. In 2018 1st International Con- ference on Data Intelligence and Security (ICDIS), pages 17-23.
A.2
41 Most Offensive American English Words Reported by Bergen (2016)
Keita Kurita, Anna Belova, and Antonios Anastasopou- los. 2019. Towards robust toxic content classification. ArXiv, abs/1912.06872.
asshole, bastard, bitch, blowjob, buttfuck, chink, clit, cock, cocksucker, cunt, dick, dumb, dyke, fag. fuck, gay, goddamn, gook, homo, hooker, kike, lesbo, loser, moron, motherfucker, nigger, nutsack, prick, pussy, queer, retard, rimjob, shit, shithead, skank, slut, sodomize, spic, tits, twat, whore
Thai Lc, Jooyoung Lec, Kevin Yen, Yifan Hu, and Dong- won Lee. 2022. Perturbations in the wild: Leveraging human-written text perturbations for realistic adver- sarial attack defense. In Findings of the Asso- ciation for Computational Linguisties: ACL 2022, pages 2953-2965, Dublin, Ireland. Association for Computational Linguistics.
A.3 Codebook for Annotating Sample of the OTH Dataset
Joon Sem Lec, Yam Gui Peng David, and Jin Hao Chan. 2020. Phishgan: Data augmentation and identifica- tion of homoglpyh attacks. In CCCT.
1. English Lanugage Body Text Includes predominately English language body text. O=No 1=Yes
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zeullmoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized BERT pretraining approach. CoRR, abs/1907.11692.
2. Sexually Explicit Content Contains graphic sexual description of geni- talia or sex acts, such as intercourse, oral sex, and masturbation.
Nikola Ljubešić Darja Fišer, and Toma? Frjavec. 2019. The frenk datasets of socially unacceptable discourse in slovene and english. In International Conference on Text, Speech and Dialogue.
O=No Pranav Maneriker, Jack W. Stokes, Edir Garcia Lazo, Diana Carutasu, Farid Tajaddodianfar, and Arun Gu- rurajan. 2021. Urltran: Improving phishing url de- tection using transformers. In MILCOM 2021 2021 IEEE Military Communications Conference (MIL- COM), page 197-204. IEEE Press.
1=Yes
References to Violence or Aggressive Acts References physical violence or aggressive behavior.
Hiroaki Suzuki, Daiki Chiba, Yoshiro Yoneya, Tatsuya Mori, and Shigeki Goto. 2019, Shamfinder: An au- tomated framework for detecting idn homographs. In Proceedings of the Internet Measurement Confer- New York, NY, USA.
0=No
1=Yes
4. Offensive Content
ence, IMC 19, page Association for Computing Machinery.
Contains profanity rude language. This category includes hate speech, as well as any other content that offensive. O=No 1=Yes
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. 2021. Learning from the worst: Dy- namically generated datasets to improve online hate detection. In ACI.

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5. Hate Speech
O=No
Contains rhetoric that is derogatory or pro- motes, rationalizes, or reinforces hatred to- wards a target group or individual based on protected characteristics. Protected character- isties include gender, race/ethnicity, LGBTQ+ identity, nationality, disability status, and re- ligion. Slurs that are used to attack people based their protected characteristics should be classified as hate speech. O=No 1=Yes
1=Yes
A.4 Annotators for Random Sample of the OTH Dataset
The lead investigator and experienced annotator coded the annotated sample of the OTH Dataset. No identifying information from the annotators was collected.
A.5 Intercoder Agreement
Intercoder Agreement (%)
Cohen's Kappa
Code
95% C1
6. Category of Hate Speech (Select All That Ap- ply)
Hate speech
97.14
0.94
0.01
0.91
0.97
Gender
Complete only for tweets classified as hate speech (Codebook Section 5).
LGBTQ+
98.21
0.81
0.08
0.64
0.97
Race
98.93
0.84
0,09
0.66
1.00
Disability
99.64
0.89
0.12
0.67
1.80
Religion
99.64
0.80
0.20
0.41
1,00
(a) Gender hate speech pertaining to group of people classified under spe- cific gender identity.
Nationality
Offensive content
99.56
0.91
0.08
0.80 1.00
Semually explicit
96.44
0.89
References violence
98.43
0.82
0.05
0.71
0.92
or aggressive Fnglish-langunge
O=No 1=Yes
body text
100.00
1.00
0.00
1.00 1.00
"No tweets containing speoch related nationality identilied in the annolated sample.
Complete only for tweets classified as gender hate speech
Table 5: Intercoder agreement by annotation code in the annotated sample (n=700) of Offensive Tweets with Homoglyphs (OTH) Dataset.
i. Misogyny hate speech that exhibits hatred of, contempt for, prejudice against women
O=No
A.6 Evaluation Existing Normalization Tools
1=Yes
(b) Race/Etthnictyy hate speech pertaining group of people who possess shared cultural background.
Unidecode library* focuses on transliteration conversions rather than homoglyph normal- ization. This introduces several character edge cases. For example, the Cyrillic "n" (U+043F), which classified homoglyph for the Latin "n" (U+006E)) by the Unicode Confusable standard, is normalized by Unide- code to the Latin letter "p" (U+0070).
O=No
1=Yes
(c) LGBTQ+ identity hate speech directed the LGBTQ+ community.
O=No
1=Yes
(d) Nationality hate speech pertaining to people from a specific nation.
2. cyrtranslit librarys only performs bi- directional Cyrillic Latin text and vice versa. As shown in Table 2, the OTH Dataset con- tains 1,182 unique non-Cyrillic Unicode char- acters.
(=No
1=Yes
(e) Disability Status hate speech pertaining people who have physical or mental conditions that limits their movements, senses, or activities. O=No
3. confusable htwpuuuaaaeehoooollglaaeeel homoglyphs library focuses on confusable detection as opposed homo- glyph normalization. It offers no normaliza- tion function/utility.
1=Yes
(f) Religion hate speech pertaining to the worship of God or other superhuman en- tities.
http::///pp..er//ooo/////rrrrss httpe:://pppi.or/ppppett///ofooe homoglyphs/

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4. confusables library' is an expanded version of the confusable_homoge yoho library, and includes a normalization function. But struggled to normalize select characters such as the Cyrillic "n" (U+043F), a homoglyph for the Latin "n" (U+006E).
and Rounds 1, 2, and of the Dynamically Generated Hate Speech Dataset (Vidgen al., 2021)
A.9
Language Identifiers Assigned by Twitter to Dataset
5. homoglyphs library includes homoglyph normalization capabilities via to_ascii () function. Unfortunately, to_ascii () deletes any characters "which can't 't be converted by default." This resulted in the deletion of most Cyrillic homoglyphs.
OTH Dataset
Annotated
Language Identifier
Language
Sample
N=90,788
n=700
Czech
62.04%
60.50%
English
24.27%
21.00%
und
Undetermined
5.20%
0.00%
Russian
5.09%
4.00%
Vietnamese
4.10%
6.50%
A.7 Computational Costs of Evaluating and Fine- Tone-Tuning Models
Estonian
0.31%
0.00%
pt
Portuguese
0.21%
0.00%
uk
Ukrainian
0.18%
0.50%
Turkish
0.13%
1.00%
Due to the small size of the annotated sample, an estimated maximum four total hours of GPU usage necessary for this study. Models were ca
Arabie
0.13%
0.50%
eu
Basque
0.09%
0.00%
Catalan
0.09%
0.50%
Tagalog
0.08%
0.00%
using Cloud-based GPU resources.
Spunish
0.08%
Indonesian
0.08%
0.00%
A.8 Selected Open-Source Transformer-Bast Based Hate Speech Detection Models
French
0.06%
0. 0.00%
bg
Bulgarian
0.04%
0.00%
to
Romanian
0.04%
0. 0.00%
1. RoBERTa-base binary classification model trained on 58 million tweets (Barbieri et al., 2020)
Dutch
0.04%
0.00%
de
German
0.03%
0.00%
Serbian
0.02%
0.00%
fi
Finnish
0.02%
0.00%
is
Icelandic
0.02%
0.00%
2. RoBERTa-base binary classification model by Liu et al. (2019) trained on the English subset of the FRENK Dataset (Ljubešić et al., 2019)
pl
Polish
0.02%
0.50%
qst
Very short text
0.01%
0.00%
No linguistic
zxx
content
0.01%
0.00%
ght
Hashtag only
0.01%
0.00%
3. BERT-base binary classification model trained on data from Twitter and Stormfront, a pop- ular white supremacist forum (Aluru et al., 2020)
hu
Hungarian
0.01%
0.50%
zh
Chinese
0.01%
0.00%
Italian
0.01%
0.00%
Danish
0.01%
0.00%
Thai
0.01%
0.00%
Swedish
0.01%
0.00%
4. RoBERTa-base binary classification model trained on 11 English hate speech datasets (Vidgen et al., 2021)
Haitian
0.01%
0.00%
Latvian
0.01%
0.00%
qme
Media
0.01%
0.00%
Slovenian
0.01%
0.00%
no
Norwegian
0.00%
0.00%
5. RoBERTa-base binary classification model trained on 11 English hate speech datasets and Round of the Dynamically Generated Hate Speech Dataset (Vidgen al., 2021)
cy
Welsh
0.00%
0.00%
It
Lithuanian
0.00%
0.00%
ur
Urdu
0.00%
0.00%
hi
Hindi
0.00%
0.00%
hy
Armenian
0.00%
0.00%
6. RoBERTa-base binary classification model trained on 11 English hate speech datasets and Rounds and 2 of the Dynamically Generated Hate Speech Dataset (Vidgen et al., 2021)
Table 6: Language identifiers assigned by Twitter
tweets in the Offensive Tweets with Homoglyphs (OTH) Dataset and the annotated sample.
7. RoBERTa-base binary classification model trained on 11 English hate speech datasets


Reviews:
[{"review_id": "5zNEKwgvsA", "reviewer": "Reviewer_KfRS", "summary": "", "questions": "1)Why did the author choose a sample size of 200 for the annotation?\n2) What is the inter-annotator agreement?\n3) What is the overall value of Cohen’s Kappa?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your time and attention, and the opportunity to respond to your questions:\n\nThe overall inter-annotator agreement in the annotated sample (n=200) reported in the paper was 97.64%. Inter-annotator agreement by annotation category ranged from 94.5% (hate speech) to 100% (English-language body text). We will add a column to Table 5 in Appendix A.6 to report this data. \n\nThe Kappa across all annotation categories was 0.953 (SE = 0.008; 95% CI = 0.937 - 0.969). [Note: This Kappa seems high due to the increase in coding decisions resulting from the cumulative assessment of intercoder reliability, but this number is accurate].  We will add the overall Kappa to the paper.\n\nWe agree that the size of the annotated sample was small; however, it was adequate to evaluate the models. Since the submission of the paper to EMNLP, we have annotated an additional 500 tweets, and the results are consistent with those presented in the paper—48.4% of the 500-tweet sample were classified as hate speech (compared with 45.0% of the 200-tweet sample reported in the paper). If the paper is accepted, we can revise Table 5 Appendix in A.6 to include results of all 700 available annotations. Further, we will rerun the evaluation of the models using the 700 annotations.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Proposed and benchmark offensive tweet dataset containing homoglyphs.\n- Develop a  character substitution scraping method to sample offensive tweets containing homoglyphs\n\n", "reasons_to_reject": "- I do not see any novelty in the proposed classifier models that are used to create the benchmarks,\n- The size of labeled data is very small (only 200 tweets).\n", "questions_to_authors": "1)Why did the author choose a sample size of 200 for the annotation?\n2) What is the inter-annotator agreement?\n3) What is the overall value of Cohen’s Kappa?", "paper_topic_and_main_contributions": "In this paper, the authors consider potential hate speech tweets containing homoglyphs. The authors develop a character substitution scraping method to sample offensive tweets with the Homoglyphs (OTH) Dataset. The authors reported that transformer-based hate speech classifiers perform poorly under zero-shot settings. Text normalization improves the performance of these models. The authors also concluded that training the model with labeled data improved its performance substantially. ", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "bLe0S3YQFT", "reviewer": "Reviewer_GyN8", "summary": "", "questions": "A. As the authors pointed out, a large chunk (88.89%) of the dataset is misogynistic in nature. It would be interesting to see how a misogyny classifier would perform.\n\nB. It is interesting to see the Czech language to be in the majority. Any particular reason for that?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your thorough analysis of the paper and the opportunity to address your concerns:\n\nReasons to Reject\n\nA.    While Unidecode performs homoglyph normalization (including Cyrillic to Latin characters), the accuracy varies from homoglyph to homoglyph. For example, the Cyrillic “п” character (U+041F), which is classified as a homoglyph for the Latin “n” character (U+006E) by the Unicode Confusable standard, is normalized by Unidecode to the Latin letter “p” (U+0070). Similar issues were associated with other existing normalization tools (see #3 in response to Reviewer BrA1). For these reasons, automated normalization was not used in the present study.\n\nThe poor performance of existing homoglyph normalization tools and our decision not to use one should be explained in greater detail in the paper, and we will add this information, if the paper is accepted. Additionally, we will include an evaluation of the Unidecode library’s normalization capabilities on the annotated sample of our dataset and subsequent performance of the models on this text (normalized by Unidecode).\n\nB.    We agree that providing verbatim tweets would strengthen the paper. If the paper is accepted, we will include tweets which exemplify the annotation categories (e.g. hate speech, references to violence, etc.).\n\nC.    We will add language to the paper to draw a clearer distinction between the capture and application of offensive text and hate speech in the present study. In short, offensive search terms with Cyrillic homoglyphs were used as “seed” to assemble a dataset of hate speech containing a much broader range of homoglyphs. However, the capture of non-hate speech containing offensive words facilitated the evaluation of the models in that it provided negative cases.\n\nQuestions\n\nA.    We agree that evaluating the performance of misogyny classifiers would be an interesting addition to the paper. In order to provide an additional zero-shot baseline, we will include results with the model developed by Saha et al. (2018), which had the highest performance on the 'Automatic Misogyny Identification' task at EVALITA-2018 (Paper: https://arxiv.org/abs/1812.06700). \n\nB.    Regarding the misclassification of English text as Czech text when homoglyphs are present—this issue appears to be Twitter specific. Unfortunately, Twitter does not provide information on how it selects language identifiers for tweets, so we cannot provide a definitive answer. It may be related to the global region from which Twitter believes a tweet was posted.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A. The dataset could be a valuable contribution to the fight against hate speech.\n\nB. It's interesting to see that the existing models were not able to perform well in the presence of such homoglyphs.", "reasons_to_reject": "A. The authors claim that there is currently no library which automates the conversion. This is not true. The Python library unidecode (https://pypi.org/project/Unidecode/) does a pretty good job.\n\nB. The presentation needs to be improved. The authors should provide some sample text for the reviewers to understand the dataset. If possible, a sample should be provided online.\n\nC. The author needs to clearly separate hate speech from offensive speech. Many of the keywords used for the search query might be considered offensive but not hate speech.", "questions_to_authors": "A. As the authors pointed out, a large chunk (88.89%) of the dataset is misogynistic in nature. It would be interesting to see how a misogyny classifier would perform.\n\nB. It is interesting to see the Czech language to be in the majority. Any particular reason for that?", "paper_topic_and_main_contributions": "The paper develops a scraping method which can collect hateful/offensive tweets which contain homoglyphs. The authors then annotate a subset of the dataset and show that existing models do not perform well on the new dataset. Upon substituting the Cyrillic characters with their Latin counterparts, the performance improves. The main contributions of the paper are the homoglyphs dataset.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "None"}, "extra_text": {}}, {"review_id": "s3OpkfW9hc", "reviewer": "Reviewer_BrA1", "summary": "", "questions": "- Why the author decide to collect data from  create query terms derived from the 41 most offensive American English words.\n- The authors state in the Section 3.1 “We also calculated the number of unique non-Latin characters (emojis excluded) present in the dataset and the number of times they were detected”. Please clarify why this sentence is stated here and where the calculated results is presented.\n- In section 3.2, author state that \"no library currently exists which automates the conversion of every homoglyph into the Latin characters they resemble\". However, I found the publicy project that provide a big list of homoglyphs and code to detect them (https://github.com/codebox/homoglyph). So, what is the different between the contribution of authors with this project.\n- The authors should provide detailed information regarding the process and properties of the three dataset configurations used for experiments.\n- In order to demonstrate that the dataset is useful, comparison to some popular datasets is necessary to highlight the different dimensions that the corpus can contribute to which kind of research.\n- It would be valuable for the authors to explain the decision-making process behind the use of the baseline models. This would help researchers to understand the benefits and limitations of these models and potentially apply them to their own research.\n- The authors present the performance of models in Table 2, 3, which is significant. Likewise, it would be interesting to explore similar results comparing monolingual and multilingual models.\n- Please include annotation guidelines that could help other researchers develop similar guidelines for Hate Speech Masked by Homoglyphs in other languages/domains, etc.", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the detailed review of the paper. We are happy to answer your questions:\n\nA. The Bergen list of 41 offensive words (2016) was chosen as it provides empirically established offensive terms. While other lists of offensive terms are available (e.g. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words), we found no other list that had been tested with human respondents.\n\nB. The quote from Section 3.1 was provided to explain our approach (methods). Then, we present these data in the Results (first paragraph of Section 4.1). We are happy to remove this sentence from Section 3.1.\n\nC. While our work discusses homoglyph normalization, the key contribution of our study is the creation of a novel dataset containing offensive text with natural homoglyph noise. Additionally, the resource you provided, while large, is not focused on homoglyphs for Latin letters and misses potential edge cases such as the runic “ᚹ” character (U+16B9) as a homoglyph for the Latin letter “p” (U+0070). Similar issues associated with other existing normalization tools were identified (see Response A to Reasons to Reject from Reviewer GyN8).\n\nD. The reviewer’s suggestion is well taken. In order to support reproducibility, we will update the paper to include figures and data visualizations such as a histogram of the Cyrillic characters, a histogram of the offensive words, and a mapping of the observed offensive words with homoglyphs to their normalized counterparts. Additionally, we will add information about the fold size and creation process for the 5-fold cross validation used in this study.\n\nE. As we mention in the Related Work section, other datasets containing homoglyphs in offensive language have been created, such as the work done by Kurita et al. (2019). However, our study is the first to produce a dataset composed entirely of natural homoglyph-laden offensive text to our knowledge. \n\nF. The seven models were chosen based on their open-source availability and recent investigations in hate speech detection. We focused on models that were trained (at least in-part) with organically generated data collected from social media platforms—specifically Twitter. We will expand the discussion of our model selection, if the paper is accepted.\n\nG. We agree that comparing monolingual and multilingual models would be interesting and warrants a future study.\n\nH. The complete annotation guidelines provided to the annotators are in Appendix Section A.4.\n\nI. Thank you for the suggested Typos, Grammar, Style, and Presentation Improvements. If accepted, we will adopt your recommendation to relocate Section 3.3 to the Appendix and include more precision for the analysis of the results. As stated in our Ethical Consideration, we will provide the dataset to researchers who request it.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper introduces the Offensive Tweets with Homoglyphs (OTH) dataset, which has the potential to serve as a novel benchmark for Hate Speech Detection obscured by Homoglyphs.\n\n- In addition to providing this dataset, the paper presents a series of compelling experiments with thorough analyses. The authors also offer valuable insights into potential directions for future research in their analysis.", "reasons_to_reject": "- Why the author decide to collect data from  create query terms derived from the 41 most offensive American English words.\n- The statement in Section 3.1 (line 138) regarding the calculation of the number of unique non-Latin characters (excluding emojis) in the dataset, and their frequency of occurrence, should be clarified, along with the presentation of the calculated results.\n- In Section 3.2, the authors claim that no existing library automates the conversion of homoglyphs into Latin characters they resemble. However, a publicly available project (https://github.com/codebox/homoglyph) provides a significant list of homoglyphs and code for detection. The authors should explain the differences between their contribution and this existing project.\n- Detailed information about the process and properties of the three dataset configurations used for experiments should be provided to ensure transparency and reproducibility.\n- In order to demonstrate that the dataset is useful, comparison to some popular datasets is necessary to highlight the different dimensions that the corpus can contribute to which kind of research.\n- It would be valuable for the authors to explain the decision-making process behind the use of the baseline models. This would help researchers to understand the benefits and limitations of these models and potentially apply them to their own research.\n- The authors present the performance of models in Table 2, 3, which is significant. Likewise, it would be interesting to explore similar results comparing monolingual and multilingual models.\n- Please include annotation guidelines that could help other researchers develop similar guidelines for Hate Speech Masked by Homoglyphs in other languages/domains, etc.", "questions_to_authors": "- Why the author decide to collect data from  create query terms derived from the 41 most offensive American English words.\n- The authors state in the Section 3.1 “We also calculated the number of unique non-Latin characters (emojis excluded) present in the dataset and the number of times they were detected”. Please clarify why this sentence is stated here and where the calculated results is presented.\n- In section 3.2, author state that \"no library currently exists which automates the conversion of every homoglyph into the Latin characters they resemble\". However, I found the publicy project that provide a big list of homoglyphs and code to detect them (https://github.com/codebox/homoglyph). So, what is the different between the contribution of authors with this project.\n- The authors should provide detailed information regarding the process and properties of the three dataset configurations used for experiments.\n- In order to demonstrate that the dataset is useful, comparison to some popular datasets is necessary to highlight the different dimensions that the corpus can contribute to which kind of research.\n- It would be valuable for the authors to explain the decision-making process behind the use of the baseline models. This would help researchers to understand the benefits and limitations of these models and potentially apply them to their own research.\n- The authors present the performance of models in Table 2, 3, which is significant. Likewise, it would be interesting to explore similar results comparing monolingual and multilingual models.\n- Please include annotation guidelines that could help other researchers develop similar guidelines for Hate Speech Masked by Homoglyphs in other languages/domains, etc.", "paper_topic_and_main_contributions": "This study introduces Offensive Tweets with Homoglyphs (OTH), a pioneering benchmark dataset for Hate Speech Detection concealed by Homoglyphs in code-mixed language. The authors evaluated the performance of seven transformer-based hate speech detection models and observed a significant improvement in detection by normalizing the data (F1 scores ranging from 0.64 to 0.74). These findings demonstrate the feasibility of collecting a dataset comprising both known and unknown homoglyphs, and the efficacy of training neural models to identify camouflaged real-world hate speech.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Section 3.3 could be placed in appendix. Instead, the authors should include statistics on the dataset's labels, distributions, and characteristics. \n- To make the analysis of the results more transparent, authors should present their results in several metrics and as a 4-digit decimal (ex: 0.81xx).\n- Please Release the dataset into the public domain. \n- Please Release the code in the public domain."}}]

Author rebuttal:

Meta-review: Overall Assessment: The paper titled "Offensive Tweets with Homoglyphs: A Benchmark Dataset for Hate Speech Detection" presents a novel dataset and associated experiments for hate speech detection in tweets containing homoglyphs. The reviewers have provided diverse opinions on the paper, which warrant careful consideration.

Scores: Based on the provided reviews, the average Soundness score is approximately 3 (Good, with some minor support needed). The average Excitement score is 3 (Ambivalent, with merits but key weaknesses). These scores indicate a paper with valuable contributions but room for improvement.
Decision: Accept-Findings