G oogle-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text (2021)

Abstract: WARNING: This article contains contents that may offend the readers. Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as ‘Peers’ and ‘Others’. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.

## introduction noisy text usually originates in unintentional errors in the conversion process of speech or signal. they are also observed in the digitized text production along with spelling errors or grammar mistakes @xcite . however, sometimes noises are intentionally inserted by authors. for instance, airbnb users write reviews in a code-mixed manner to convey negative information about the accommodations to other users who share the language without being offensive to the foreign host. in another viewpoint, users who share the social identity of a specific community such as reddit or 2ch create and use memes for fun, which play a role as jargon in those communities @xcite . also, the users of broadcasting platforms such as twitch, or online games, use profanity terms in an undetectable way @xcite , to express their emotions and at the same time avoid the detection of automatic censoring systems @xcite . in this study, we investigate and formulate the stakeholders of such strategies, namely the author, other users in the community who should understand the text, and the ones who should not understand the text. though writing and posting text is done by the author, the other two influence how the avoidance strategies are represented. in other words, the intention of generating noisy text varies depending on the existence of each stakeholder, and this variation may also affect the strategy used. this typology has been domain specifically studied in online censorship and resistance @xcite @xcite , but we focus more on the generalized agents of this 'text gaming' @xcite . we conduct an in-depth analysis to make up a transferable taxonomy. our target domain is the korean online space, where web texts are actively generated and rapidly spread across various communities. we intend to provide a unified taxonomy of posting typology and avoidance strategy, based on the observation of online space expressions. our contribution is as follows: diverse strategies have been suggested for text hiding, and they can be acknowledged as linguistic and non-linguistic ones. non-linguistic strategies are studied in coding theory, and the encoded contents may not be comprehended by humans @xcite ; taleby @xcite . in agarwal (2013), various strategies for steganography are reviewed and compared, and the non-linguistic approaches adopt codewords @xcite , unicode @xcite , word replacement (ahvanooey et al., 2018), etc., that mainly aim to transmit the message without being exposed. in contrast, linguistic strategies are often visible to humans. those include sentence-level order replacement @xcite , pragmatic transformation @xcite , or transliteration @xcite . taleby ahvanooey et al. (2019) classified those approaches into semantic, syntactic, and statistic ones, while they were studied in view of steganography rather than intentional text noise. @xcite has handled the issue in a manner closer to our approach, listing up strategies such as deletion of characters, phonetic substitution, abbreviation, using dialects, etc. however, it is less concerned with why the users adopt such techniques, considering possible stakeholders or pragmatic context. in a wider view, the topics above are closely related to avoidance strategies and code-switching, which are mainly studied in sociolinguistics in terms of language varieties and community jargon. 1 famous examples are 'mother-in-law' codes used among dyirbal language of northern queensland, where everyday speech style guwal is replaced by an 'avoidance' style jalnguy when certain opposite-sex relatives (especially in-law) are present, maintaining the syntax but alternating the lexical items @xcite . similar happens among basque-spanish bilinguals, where linguistic codeswitching takes place to fill lexical gaps, convey certain attitude, or smooth negative connotations @xcite . these cases refer to when the utterance is modified to fit the proper style of the speech community. besides, some code-switching may intend to hinder the speaker's genuine message to specific listeners. in previous studies on online censorship and avoidance strategies @xcite , the authority that hacks the original text is usually assumed as government. moreover, such authority is not the only factor that users generate noisy text in the online space; meme is a representative case @xcite that games text with less consideration on being censored. we would like to present a typology that can encompass those phenomena. we focused on that these strategies are observed more frequently beyond the latin alphabet writing system due to the usefulness of code-mixing and transliteration. accordingly, we qualitatively analyze the korean online space, where the usage of the featural writing system hangul and english code-mixing are both active @xcite . in specific, the featural writing system is advantageous in diverse phonological representations and the agglutinative nature of korean allows flexible substitution of code-mixed words. ## observation we demonstrate how our observation of korean online space had led us to set stakeholders of intentionally noisy texts. we build a typology based on them and link it with specific linguistic strategies. ## unified taxonomy combining the materials in the previous sections, we made up a table for the unified taxonomy of intentionally noisy user-generated texts ( trick words, as mentioned earlier, have a purpose of conveying information to peers, but not to others. thus, most of these texts contain messages that are adversarial to others (i.e., censoring 3 we adopt strategies used for analyzing the text of korean users in airbnb review posts and apply it to our taxonomy. these strategies are roughly explained here to help the readers understand, but are to be published as a separate article @xcite . meme words share the purpose of tricks in conveying information to peers, but others to whom their exposure should be avoided is absent. here, peers can be viewers watching the same broadcast, members of a community, or people in a group. the meme is often to share pleasure with them. filler words are not intended to convey information to peers, but are used to avoid censorship. again, this can be viewed as a kind of exclamation, and it is different from 'tricks' in that 'fillers' does not necessarily consider the communication with peers. '존웃' (≈ lmfao) and '이런 ㅁ ㅊ' (≈ wtfk) are usual terms in chats or comments, but they do not necessarily contain semantics that should be protected or conveyed. 'ㅆ1발', '^^ㅣ발', and 'tlqkf' all indicates '씨발' (fxxk), which are written with optical modification, semantic trick (with positive emoji ^^), or typing korean in english qw-erty mode. these examples do not head other users, but are under censorship of the system. unfortunately, codes were difficult to be unified within the adopted strategies. currently, such texts are more adequately dealt with within coding theory. however, given that recent studies focus on linguistic approaches of text steganography @xcite , incorporating these in our taxonomy is arranged as future work. discussion though we categorized the expressions according to the taxonomy, we have not yet investigated the property of each category in detail. one phenomenon displayed is the temperature of expressions in each posting type. tricks and fillers should avoid the censorship of the authority, that the expressions tend to include social taboos such as profanity terms, swear words, or unethical contents. among them, tricks tend to be more informative than fillers since they have a particular purpose of conveying information to peers, in the form of advertisement, secret message, or insulting. it is one of the reasons that more creative avoiding strategies are observable in tricks rather than in fillers, and thus easier to find diverse examples. such creativity is also actively exhibited in memes, while the aspect is more tilted to pun and that they do not have to take into account others' monitoring. in total, tricks show hostility towards or harms others or peers, fillers may alert others since they contain profanity terms, but has no particular target. memes are usually punned terms not showing explicit hate, but may contain a cultural or societal bias towards a certain group of people or a person. limitation though we are empirically aware that tricks, memes, and fillers are prevalent in the online world, used for various purposes, we have yet constructed an annotation scheme or a corpus that lists up detailed typology and corner cases. it is an essential but highly consuming process, which depends on languages and communities. however, we believe that the characteristics of each text posting type discussed above would help us build a thorough annotation scheme concerning a variety of language phenomena. we expect that constructing such schemes would benefit a lot from our categorization, strategies, and examples, at least in selecting the online space or communities to collect the raw corpus from. ## conclusion in this paper, we scrutinized the posting types and avoidance strategies of noisy web text in the korean online space. these approaches differ from coding theoretic methodologies such as steganography and more concentrates on decoding using human text understanding. the taxonomy was defined upon the presence of wanted and unwanted readers of the noisy text, and this typology is expected to be transferable to various languages and cultures. a limitation of our study is a lack of quantitative analysis. as a first step, we aim to annotate the texts collected from various online spaces, e.g., hate speech or biased text, using the built taxonomy. such application will show how and why toxic comment authors trick the monitoring algorithms, at the same time allowing us to see the correlation between purposes and strategies. we hope our taxonomy can be consolidated as a useful tool to analyze intentionally noisy user-generated texts in online communities.