The State of Profanity Obfuscation in Natural Language Processing Scientific Publications (2023)

Abstract: Work on hate speech has made considering rude and harmful examples in scientific publications inevitable. This situation raises various problems, such as whether or not to obscure profanities. While science must accurately disclose what it does, the unwarranted spread of hate speech can harm readers and increases its internet frequency. While maintaining publications’ professional appearance, obfuscating profanities makes it challenging to evaluate the content, especially for non-native speakers. Surveying 150 ACL papers, we discovered that obfuscation is usually used for English but not other languages, and even then, quite unevenly. We discuss the problems with obfuscation and suggest a multilingual community resource called PrOf with a Python module to standardize profanity obfuscation processes. We believe PrOf can help scientific publication policies to make hate speech work accessible and comparable, irrespective of language.

## introduction a major downside of unsavory research subjects is that they still need to be investigated and reported, especially if we want to improve matters. hateful language poses this challenge in natural language processing. we first need to collect and annotate it to detect, classify, and mitigate it. setting aside the ethical conundrum of subjecting annotators to hateful language @xcite , reporting on it presents researchers with various challenges. on the one hand, science should unflinchingly report on its subject matter, no matter how unpleasant @xcite . on the other hand, if that subject matter is language, then reporting on it is almost equivalent to producing it. this issue presents two problems: 1) proliferation and 2) audience framing. a political h*mo? i am not listening to a fairy gay f*ggot [...] @xcite suck a pig d*ck c*nt @xcite bruh im tired of n*ggas [...] @xcite someone should r*pe her @xcite context should disambiguate whether a word is used in its intended meaning or as a meta-function of talking about the word @xcite without using its original intent. however, written content that is freely accessible online might appear in unexpected contexts. e.g., as training data for language models @xcite . open access also means it is unclear who will read a given text. scientific readers will likely assume the meta-function and discount hateful language. however, there is no guarantee as to who other readers may be. disclaimers can help frame this problem and give the reader a choice. however, readers who do not want to read unpleasant examples should not be excluded from conducting research in hate speech detection. disregarding their personal feelings about an offensive term seems cruel and insensitive at best, especially if they are members of targeted groups or abuse victims (see a compromise solution is obfuscation, where one or more letters in an offensive term are replaced with stars or other symbols. this approach preserves the word shape and allows interested readers to reconstruct the original word without allowing it to proliferate or forcing it upon readers. however, based on our survey of 150 nlp papers, there are several issues with obfuscation: this prompts one simple question: how can we use profanity obfuscation in scientific publication? among the 150 *acl publications from 2021 reporting profanities, a number of solutions emerge. even if standardization has been made in specific venues foot_1 , these solutions are not consistent. as outlined above, obfuscation leads to several unintended problems, predominantly for nonnative speakers. as the nlp community grows, an increasing number of readers face this conundrum. n**ger is easy enough to guess, but p*cker is difficult without advanced knowledge. or, if you are a native speaker of english, consider danish p*rker, german f*tz*, or italian bo***ino. now try googling them. moreover, many slurs and insults are culture-specific. for example, without knowledge of the history of racism in america, it is almost impossible to even guess at the meaning of c**n. this issue is related to the bias towards work on english in nlp @xcite . on common procedures for profanity obfuscation. prof will permit researchers to report profanities in scientific publications while ensuring formal appearance, readability, and accessibility. research in hate speech inevitably needs to face the use of profanities in language. these taboo words are known to be perceived negatively @xcite , leading to heightened states of emotional arousal @xcite , and potentially causing vicarious trauma @xcite . as scholars publishing on open access platforms, we need on the one hand to protect readers from this content and, on the other hand, to report the message in its unexpurgated entirety (jane, 2014) because euphemisms and generic descriptors cannot convey the hostility. obfuscation, if not ideal, is the best compromise to deal with the conundrum of hateful language in scientific literature: obfuscated words should be discernible to allow accessibility and replicability. how can a researcher test the same examples if it is not possible to discern the text? at the same time, while the content deciphering is left to the reader, the conveyed emotion is still negative @xcite . ## methodology and results we surveyed the acl anthology for proceedings of *acl conferences that took place in 2021 and a workshop specifically focused on abuse detection, the workshop on online abuse and harms (woah). we searched this data for occurrences of "*" and "#", used to obfuscate profanities. 3 for each paper that included one or more profanities, we noted whether or not the authors notify the use of offensive language and which languages the authors considered in their offensive examples. each conference's profanity count is listed in ## prof we propose prof, a multi-lingual community resource for the obfuscation of profanities in scientific publications. it allows for the uninterrupted reading of papers with profanities while allowing non-native speakers to look up words and definitions if they desire. for the definition, we use the multi-lingual babelnet @xcite . prof consists of a table reporting: 1) the unobfuscated profanity (e.g., fuck) 2) first-vowel obfuscation (e.g., f*ck) 3) the language (e.g., english) 4) the part-of-speech (pos) tag (e.g., noun) 5) the babelnet multi-lingual synset (e.g., https://babelnet.org/synset?@xmath0: 00006453n&@xmath1. we suggest the obfuscation practice of removing the first vowel. for compound words, we obfuscate the first vowel of the element with an offensive meaning (e.g., femin*zis). we extend prof to other languages with the help of native speakers, reaching a total of 203 profanities: 50 in english, 44 in french, 19 in german, 42 in italian, and 48 in spanish. details about prof construction are given in appendix 4. we understand that our work is currently limited to the profanities of the languages we speak and the set of profanities we cover. however, we currently cover all the profanities reported in ~3000 published papers. as we advance, we hope that prof will be used as a community-based research tool that evolves in conjunction with the research conducted on it. therefore, we aim for a crowdsourcing strategy. we recommend that researchers submit their entries to the project repository, which the authors of this paper will maintain. this method ensures the repository is comprehensive and up-todate while also facilitating access. prof construction prof construction starts with the list of english profanities surveyed from recent proceedings of *acl conferences (see section 3). this starting list comprises 50 entries, of which 37 unique terms and 4 unique pos tags (adj, adv, noun, verb). note that profanities can be associated with different pos tags (e.g., f*ck can be a noun, a verb, and an adverb). we used these 50 english profanities as a seed for creating german, italian, french, and spanish prof. using babelnet, we retrieve all associated concepts in other languages for each profanity. note that each language is characterized by a different number of profanities that can be associated with a target group (e.g., women). using babelnet instead of a translation tool enables us to retrieve all these terms instead of just one exact translation. the limitation of this approach is that the number of retrieved concepts starting from one term is very high and not all relevant. for example, some terms can be used to refer to profane acts in some contexts, but their main meaning is non-profane (e.g., avvitare (screw) is a word that can also refer to the act of having sexual intercourse). in other cases, the profanities related concepts in babelnet are still in english or are literally translated, resulting in nonsense terms in the target language (e.g., piece of tail foot_3 is literally translated in italian with the non-existing idiom pezzo di coda). for this reason, we use a hurtful lexicon to filter retrieved related concepts @xcite . finally, we asked native speakers to validate the resulting filtered list of terms by removing terms that were not unambiguous profanities. we also permit native speakers to include additional profanities if they felt some popular ones were missing, on average they added 4 profanities. using prof we release prof as a python package 5 and web application. the package automatically obfuscates profanities starting from a string or a text file, and can reveal profanities from their obfuscated versions (see appendix b). ## related work profanities have been investigated in nlp for discovering how to automatically filter them or how to prevent their obfuscation. these issues can be solved straightforwardly with a forbidden word list. however, preparing this list is difficult, as people are constantly creating new forms to avoid filtering via dictionary lookups, such as $h!t, sh1t, or s.h.i.t. i.e, introducing spacing or punctuation between letters, swapping or removing characters, and 0-9 substitutions. approaches to automatically filter variations of vulgar words are based on string matching techniques @xcite . the research on de-obfuscation of profanities is much larger. this is due to nlp tools' need to access the content of a sentence. several studies @xcite @xcite showed that obfuscated words are often ignored or treated as out-ofvocabulary impacting tasks like sentiment analysis or hate speech detection. methods range from sequence alignment algorithms used in genomics (rojas-galeano, 2017) to word embeddings @xcite . our work differs from this literature in that we focus on scientific publications, not on social media. in this setting, the use of a dictionary is feasible. ## conclusion our work highlights the lack of obfuscation standards for reporting profanities in scientific publications. prevailing practice allows for dangerous pro-5 https://github.com/dnozza/ profanity-obfuscation cedures and restricted access. we introduce prof, a resource to standardize profanity obfuscation in scientific publications. prof allows researchers to prevent offending readers while ensuring that information is readable and accessible. we plan to expand to more languages. as researchers add new words featured in their papers, prof will grow along with the number of publications.